American Journal of Scholarly Research and Innovation
The American Journal of Scholarly Research and Innovation (AJSRI) ONLINE ISSN (3067-2163) is a leading Peer-Reviewed, Open Access International Journal dedicated to fostering innovation and excellence across a wide array of academic disciplines. The journal provides a platform for scholars, researchers, and professionals to disseminate groundbreaking research that addresses real-world challenges through interdisciplinary collaboration. As a monthly publication, AJSRI releases 12 issues annually, maintaining a consistent schedule that ensures the timely distribution of high-quality, impactful research. The journal encompasses diverse fields, including Arts, Science, Commerce, Social Sciences, Management, Technology, and Engineering, making it a truly interdisciplinary platform. AJSRI collaborates with international and national conferences to publish extended versions of selected papers, furthering the visibility and impact of researchers’ work. The journal is committed to publishing original, high-quality research that strengthens academic knowledge and drives innovation.
GLOBAL SOURCING, CYBERSECURITY VULNERABILITIES, AND U.S. RETAIL MARKET OUTCOMES: A REVIEW OF PRICING IMPACTS AND CONSUMER TRENDS
This quantitative review synthesizes evidence on how global sourcing and emerging cybersecurity vulnerabilities jointly influence U.S. retail pricing structures and consumer market outcomes. Drawing on 64 empirical studies published between 1990 and 2020, the analysis harmonizes sourcing-intensity measures across retail categories and integrates documented cyber-risk exposures within internationally distributed supply chains. The pooled data reveal substantial variation in sourcing reliance, with mean import shares of 72% in apparel and footwear, 70% in toys, 64% in consumer electronics, 55% in general merchandise, 42% in home goods, and 28% in grocery tradables. Higher sourcing intensity consistently aligned with nominal price declines in tradable goods, including average posted-price reductions of −4.8% in apparel, −4.1% in toys, and −3.5% in electronics, while grocery tradables experienced a modest −0.8% decrease. Transaction-price effects were even larger, reaching −6.2% in apparel and −5.4% in toys, indicating strong promotional pass-through. Correlational analyses confirmed these patterns: sourcing intensity was negatively associated with posted prices (r = −0.62) and price-change magnitudes (r = −0.58), and positively associated with imported-variety expansion (r = 0.66) and quality-adjusted affordability (r = 0.49). Random-effects meta-regressions further demonstrated significant price-reducing effects of sourcing intensity on both posted (β = −0.032, p < .001) and transaction prices (β = −0.041, p < .001). Exchange-rate cost shocks generated additional price decreases (β = −0.021), whereas tariff shocks produced modest increases (β = 0.012). Cybersecurity vulnerabilities—particularly data breaches, ransomware interruptions, and compromised vendor systems—emerged as a moderating factor, amplifying cost variability and temporarily disrupting price pass-through in categories with high digital supply-chain integration. Despite these risks, non-price outcomes remained substantial: sourcing intensity predicted imported-variety growth (β = 0.058) and stronger substitution patterns (β = 0.026). Overall, the evidence shows that global sourcing lowers U.S. retail prices and expands variety, while cybersecurity exposures introduce new uncertainties that increasingly shape consumer markets in the digital sourcing era.
Copyright (c) 2021 Md. Hasan Imam, Shaikat Biswas (Author)
QUANTITATIVE ASSESSMENT OF AI-ENABLED CONSTRUCTION PLANNING TOOLS FOR REDUCING DELAYS IN U.S. INFRASTRUCTURE PROJECTS
This study addresses schedule overruns in U.S. infrastructure projects and the limited quantitative evidence on whether AI-enabled construction planning tools reduce delays. The purpose is to quantify relationships between AI-based planning adoption and schedule performance using project-level data. A quantitative cross-sectional, case-based design used a Likert five-point survey of practitioners covering 198 infrastructure cases across public agencies and enterprise contractors and consultants. Key variables included AI-enabled planning tool adoption, planning quality, coordination effectiveness, project size, complexity, contract type, and a Schedule Delay Index (SDI) from planned and actual durations. Reliability was high for all multi-item scales (α = 0.84-0.91). The analysis plan combined descriptive statistics, Pearson correlations, and multiple regression with moderation tests. Projects showed moderate AI adoption (M = 3.47, SD = 0.78) and an average 11% schedule overrun (SDI M = 0.11, SD = 0.09). AI adoption correlated negatively with SDI (r = −0.41, p < .001) and remained a significant predictor of lower delay after controlling for size, complexity, and contract type; a one-point increase in adoption was associated with a 2.8 percentage point reduction in SDI. Adding planning quality and coordination effectiveness increased explained variance in SDI from 25% to 41% and partially mediated the AI-delay relationship, with effects strongest on highly complex projects. The headline finding is that AI-enabled planning tools contribute meaningfully to delay reduction when embedded in robust planning and coordination practices. The study implies that infrastructure owners should treat AI-enhanced planning as a strategic capability for improving delivery reliability across the sample.
IMPACT OF HIGH-PERFORMANCE COMPUTING IN THE DEVELOPMENT OF RESILIENT CYBER DEFENSE ARCHITECTURES
This study addresses the growing problem that many cloud and enterprise environments are investing in artificial intelligence and advanced computing for cyber defense without clear evidence of how these capabilities translate into resilient performance under attack. The purpose is to empirically examine how AI capability and advanced computing capacity contribute to cyber defense resilience in real organizational settings. A quantitative, cross sectional, case-based survey was conducted with organizations that operate AI enabled security solutions on cloud, high performance, and edge or fog infrastructures, yielding 220 usable responses from 310 distributed questionnaires (70.9 percent) from cybersecurity and IT professionals. Core variables included AI capability, advanced computing capacity, cybersecurity readiness, and a composite cyber defense resilience index. Data were analyzed using reliability and factor analysis, descriptive statistics, Pearson correlations, and multiple regression. Results show moderately high AI capability (M = 3.72, SD = 0.71) and advanced computing capacity (M = 3.85, SD = 0.66), but only moderate resilience (M = 3.61, SD = 0.69). AI capability (β = 0.39, p < .001) and advanced computing capacity (β = 0.27, p < .001) significantly predict resilience in a model that explains 61 percent of variance (R² = 0.61), with strong correlations between AI and resilience (r = .62) and computing and resilience (r = .55). These findings indicate that resilient cyber defense in cloud and enterprise contexts depends on the integrated development of AI analytics, scalable computing infrastructures, and cybersecurity readiness, offering actionable guidance for CISOs and security architects.
Copyright (c) 2021 Zamal Haider Shish, Mst. Shahrin Sultana (Author)
IMPACT OF BIG DATA AND PREDICTIVE ANALYTICS ON FINANCIAL FORECASTING ACCURACY AND DECISION-MAKING IN GLOBAL CAPITAL MARKETS
This quantitative study examined how big data intensity and predictive-analytics capability influenced financial forecasting accuracy and decision-making quality in global capital markets. The study was grounded in an extensive review of 112 peer-reviewed empirical and methodological papers spanning econometric forecasting, alternative data applications, machine learning in asset pricing, and decision-value testing across international markets. Using a cross-sectional time-series panel of market–asset–time observations, the analysis integrated developed and emerging markets and covered equities, fixed income, foreign exchange, and derivatives. Big data intensity was operationalized as a composite index reflecting data volume, variety, frequency, latency, dimensionality, and coverage breadth, while predictive-analytics capability was measured as a ranked model-class indicator ranging from classical econometrics to hybrid econometric–machine-learning systems. Forecasting accuracy served as the primary outcome and was evaluated through rolling out-of-sample errors; decision-making quality was assessed through standardized portfolio, trading, and risk-performance indicators. Descriptive results showed substantial dispersion across markets, with mean big data intensity at 0.56 (SD = 0.19) and predictive-analytics capability at 3.12 (SD = 1.09) on a five-point scale. Developed markets exhibited higher data intensity (0.63) and sophistication (3.54) than emerging markets (0.48 and 2.66, respectively). Correlation analysis indicated that big data intensity and predictive-analytics capability were each inversely associated with forecasting error (r = −0.46 and r = −0.52), and forecasting error was inversely related to decision-making quality (r = −0.48). Fixed-effects panel regressions confirmed significant direct effects: big data intensity reduced forecasting error (β = −0.021, p < .001) and predictive-analytics capability reduced forecasting error (β = −0.018, p < .001). The interaction term was also negative (β = −0.007, p < .01), indicating complementarity such that analytics benefits strengthened under higher data intensity. In the decision model, forecasting error significantly predicted decision quality (β = −28.6, p < .001), demonstrating that statistical accuracy gains translated into improved economic outcomes. Overall, the study provided integrated evidence that richer data environments and advanced predictive analytics jointly enhanced forecasting precision and decision performance across global capital markets.
Copyright (c) 2024 Md. Mosheur Rahman, Md Arman Hossain (Author)
ARTIFICIAL INTELLIGENCE APPLICATIONS FOR PREDICTING RENEWABLE-ENERGY DEMAND UNDER CLIMATE VARIABILITY
This quantitative, cross-sectional, case-study–based research investigates how artificial intelligence (AI) applications can improve renewable-energy demand forecasting under climate variability while being adopted in real operational settings. The study addresses the problem that utilities and renewable-intensive enterprises still underuse climate-variability indicators in AI models, limiting the accuracy and decision value of demand forecasts. Using a structured Likert five-point questionnaire and secondary operational and climate data from renewable-energy systems, 280 questionnaires were distributed and 214 valid responses were obtained (76.4% usable rate). Key constructs included organizational analytics capability, data quality and integration, climate-variability integration in AI models, AI model transparency, perceived forecast accuracy, trust in AI outputs, and intention to use AI-based forecasts. Reliability analysis showed Cronbach’s alpha values between 0.82 and 0.91, and multiple regression and correlation analyses were used alongside benchmarking of traditional regression and AI models. Compared with a traditional multiple regression model (MAPE 7.8%, RMSE 18.4 MW) and an AI model without explicit climate indicators (MAPE 6.1%, RMSE 15.2 MW), the climate-enhanced AI model achieved substantially lower error (MAPE 4.3%, RMSE 11.6 MW). Survey results indicated moderately high perceived forecast accuracy (mean 3.92) and trust (3.88), with intention to use AI forecasts averaging 4.03. Data quality and integration and climate-variability integration were the strongest predictors of perceived accuracy, while perceived accuracy and trust primarily drove intention to use. The findings imply that climate-aware AI forecasting delivers measurable accuracy gains but requires robust data pipelines and analytics capability to be trusted and embedded in renewable-energy planning and operations.
Copyright (c) 2024 Dr. Zulqarnain, Zayadul Hasan (Author)
INTEGRATION OF COMMUNICATIONS-BASED TRAIN CONTROL (CBTC) INTO CIVIL ENGINEERING DESIGN FOR SAFER AND CYBER-SECURE RAIL SYSTEMS
This study addresses the practical problem that Communications-Based Train Control (CBTC) technologies are often engineered separately from civil infrastructure, leaving unclear to what extent integrated design actually improves safety and cyber-secure performance in operating rail enterprises. The purpose of the research is to quantify how CBTC-civil engineering integration relates to perceived safety performance, cybersecurity resilience, and overall system performance in real CBTC-enabled corridors. A quantitative, cross-sectional, case-study based survey design was adopted, using a structured five-point Likert questionnaire administered to 220 experienced professionals in CBTC-equipped rail infrastructure managers, operators, consultants, and suppliers, yielding a 71.0% usable response rate. Key latent variables were CBTC-civil integration quality, safety performance, cybersecurity resilience capability, and overall system performance, all measured with reliable scales (Cronbach’s alpha 0.87-0.91). The analysis plan combined descriptive statistics, reliability and validity checks, Pearson correlations, and multiple regression models. Results show that integration quality is strongly associated with safety (r = 0.62) and cybersecurity resilience (r = 0.57), explaining 39% and 33% of their variance respectively, while integration, safety, and cybersecurity together explain 65% of the variance in overall performance. Safety and cybersecurity emerge as the strongest direct predictors of performance, with CBTC-civil integration exerting an additional indirect effect through these constructs, indicating that integrated civil-signaling design is an upstream driver of safer and more cyber-resilient CBTC enterprises. The findings imply that rail authorities and CISOs should institutionalize CBTC-civil co-design, security zoning, and safety-security co-engineering from the earliest alignment and station-planning stages to realize the full performance benefits of CBTC deployment.
Communications-Based Train Control (CBTC)
Copyright (c) 2023 Syed Zaki Uddin, Md Sarwar Hossain Shuvo (Author)
ARTIFICIAL INTELLIGENCE-DRIVEN DIGITAL TRANSFORMATION MODELS FOR ENHANCING ORGANIZATIONAL COMMUNICATION AND DECISION-MAKING EFFICIENCY
This quantitative study investigated how artificial intelligence (AI) capability and digital transformation (DT) maturity influenced organizational communication quality and decision-making efficiency, with communication quality tested as a mediator and DT maturity as a moderator. The literature review synthesized evidence from 68 prior quantitative papers to refine construct definitions, measurement logic, and empirical pathways. A cross-sectional survey was conducted with 412 respondents from AI-adopting organizations across multiple sectors. Descriptive results indicated moderate-to-high levels of AI capability (M = 3.71, SD = 0.64) and DT maturity (M = 3.62, SD = 0.61). Communication quality recorded the highest mean (M = 3.84, SD = 0.59), followed by decision-making efficiency (M = 3.68, SD = 0.62), and distributional diagnostics supported parametric modeling. Measurement quality was strong (Cronbach’s α = .86–.93; CR = .88–.94; AVE = .60–.70), and CFA fit was acceptable (CFI = .95, TLI = .94, RMSEA = .05, SRMR = .04). Correlations among principal constructs were positive and significant, with no multicollinearity risk (VIFs < 2.10). Structural modeling confirmed all hypothesized direct effects: AI capability positively predicted communication quality (β = .58, p < .001) and decision-making efficiency (β = .33, p < .001), and communication quality positively predicted decision-making efficiency (β = .49, p < .001). Mediation testing showed a significant indirect effect of AI capability on decision efficiency via communication quality (β_indirect = .28, p < .001), indicating partial mediation. Moderation analysis demonstrated that DT maturity strengthened the AI-to-decision efficiency relationship (β_interaction = .14, p = .001). Overall, the findings supported an integrated mediated–moderated model explaining how AI-driven digital transformation enhances communication and decision efficiency in organizational settings.
Copyright (c) 2025 Alifa Majumder Nijhum (Author)
HACCP AND ISO FRAMEWORKS FOR ENHANCING BIOSECURITY IN GLOBAL FOOD DISTRIBUTION CHAINS
This quantitative study investigated how HACCP and ISO 22000–aligned food safety management systems enhanced biosecurity performance across global food distribution chains. The analysis used a multi-country, multi-node dataset comprising 120 firms, 286 facilities, 412 international routes, 1,034 suppliers, and 6,480 shipments. Firms were distributed across HACCP-only (30.0%), ISO-only (25.8%), integrated HACCP–ISO (29.2%), and low-maturity baseline chains (15.0%). High-risk commodities represented 58.5% of shipments, and cold-chain dependency averaged 0.64, indicating extensive exposure to temperature-sensitive biosecurity risks. Preventive capability was operationalized through HACCP maturity, ISO maturity, and integration depth indices, all of which demonstrated strong reliability (α = 0.88–0.91) and coherent factor structure. Descriptively, integrated chains showed the strongest preventive profiles (HACCP maturity M = 4.12; ISO maturity M = 4.05; integration depth M = 4.21) and the most favorable outcomes, including microbial compliance of 94.3% and only 1.8 CCP deviations per 100 shipments, compared with 86.1% compliance and 5.7 deviations in baseline chains. Multilevel regressions indicated that HACCP maturity predicted higher microbial compliance (β = 0.24, p < .001) and fewer CCP deviations (β = −0.31, p < .001), while ISO maturity independently improved compliance (β = 0.19, p < .001) and reduced recalls or border rejections (β = −0.23, p < .001). Integration depth added further explanatory power across all outcomes, especially CCP deviations (β = −0.18, p < .001) and supplier nonconformity dispersion (β = −0.21, p < .001). Final models explained substantially more variance than baseline risk controls alone (R² rising from 0.12–0.25 to 0.30–0.44). Overall, higher HACCP and ISO maturity, reinforced by deep integration, aligned with stronger, more stable chain-wide biosecurity in multinational distribution corridors.
Copyright (c) 2023 Pankaz Roy Sarkar, Md. Kamrul Khan (Author)
AI-DRIVEN THREAT DETECTION AND RESPONSE FRAMEWORK FOR CLOUD INFRASTRUCTURE SECURITY
This quantitative study developed and evaluated an AI-Driven Threat Detection and Response Framework for cloud infrastructure security using a controlled multi-service cloud testbed. The review phase synthesized evidence from over 30 peer-reviewed studies addressing cloud threat surfaces, telemetry foundations, AI detection models, automated response orchestration, drift robustness, and closed-loop security control. The experiment analyzed 120,000 fixed-length telemetry windows (114,000 benign; 6,000 malicious) and 360 injected incident episodes stratified by workload volatility, identity complexity, and attack stealth. Multi-modal telemetry from IAM logs, control-plane audit trails, network flow logs, runtime metrics, and application traces was transformed into single-source, late-fusion, and early-fusion feature sets. Detection comparisons showed high overall performance (precision M = 0.91, recall M = 0.88, PR-AUC M = 0.93) with low alert noise (false alarm rate M = 0.021) and rapid detection (MTTD M = 2.8 minutes). Mixed-effects regressions indicated that deep sequence (β = 0.041, p < .001), deep graph (β = 0.038, p < .001), and hybrid ensemble models (β = 0.052, p < .001) significantly improved PR-AUC relative to supervised baselines, and early multi-modal fusion yielded the largest gain (β = 0.047, p < .001). Drift-triggered recalibration reduced detection delay (β = −0.62 minutes, p < .001) and false alarms (β = −0.006, p < .01), stabilizing performance across drift phases where PR-AUC shifted from 0.95 (pre-drift) to 0.89 (drift) and recovered to 0.94 (post-drift). Calibrated threat scores reduced false containment via significant mediation (indirect β = −0.012). Risk-weighted response decreased MTTR by 1.21 minutes (p < .001), while sequential response produced the highest containment success (β = 0.058, p < .001) with lower service-impact cost. Detection and response models explained 62% and 61% of variance in PR-AUC and containment success, supporting a quantitative closed-loop cloud defense framework.
Copyright (c) 2025 Md Harun-Or-Rashid Mollah (Author)
THERMAL & ELECTRICAL PERFORMANCE ENHANCEMENT OF POWER DISTRIBUTION TRANSFORMERS IN SMART GRIDS
This study addresses the persistent problem of thermal stress, electrical losses, and premature failures in power distribution transformers operating in smart grid environments, where dynamic loading and high reliability expectations intensify asset risk. The purpose is to quantify how smart grid oriented operational, monitoring, and maintenance practices enhance transformer thermal and electrical performance and reliability. Using a quantitative, cross-sectional, case-based survey design, data were collected from 210 engineers, operators, maintenance supervisors, and asset managers across eight smart grid enabled utility and industrial enterprises, using a five-point Likert scale instrument with construct reliabilities between 0.86 and 0.91. Key independent variables were smart monitoring and automation, thermal management and cooling practices, load management and demand response, and predictive maintenance and diagnostics, while dependent variables captured perceived thermal performance, electrical performance and efficiency, and reliability and lifespan outcomes. Data were analyzed using descriptive statistics, Pearson correlations, and multiple regression. Headline findings show all practice constructs are positively and significantly related to performance, with correlations between 0.53 and 0.71 and regression models explaining 53–60 percent of variance in outcomes; predictive maintenance (β = 0.38, p < .001) and smart monitoring (β = 0.30–0.31, p < .001) emerge as the strongest predictors of reliability and thermal performance, while thermal management most strongly predicts electrical efficiency (β = 0.34, p < .001). Organizations with more than five years of smart grid deployment report significantly higher practice levels and reliability scores (mean reliability 4.05 versus 3.71, p = .001). The findings imply that integrated investment in smart monitoring, disciplined thermal and load management, and predictive maintenance is critical for extending transformer life and improving smart grid resilience.
Copyright (c) 2023 Md Hamidur Rahman (Author)
SMART CONTINUOUS IMPROVEMENT WITH ARTIFICIAL INTELLIGENCE, BIG DATA, AND LEAN TOOLS FOR ZERO DEFECT MANUFACTURING SYSTEMS
This study addresses the practical problem that many Industry 4.0 oriented manufacturers invest separately in artificial intelligence, big data analytics, and lean tools without clear empirical evidence of how their integrated deployment supports smart continuous improvement and zero defect manufacturing outcomes. The purpose is to test a model linking AI capability, big data analytics capability, lean tools deployment, smart continuous improvement performance, and zero defect results using a quantitative cross-sectional, case-study based survey of 214 professionals from ten enterprise manufacturing plants (response rate 71.3 percent). Key variables are measured on five-point Likert scales and analyzed using descriptive statistics, reliability analysis, Pearson correlations, and multiple regression. All scales show strong internal consistency (Cronbach’s alpha 0.88 to 0.92). Lean tools deployment has the highest mean (3.94), followed by smart continuous improvement (3.78) and zero defect outcomes (3.65), while AI and big data capabilities are moderately developed (3.46 and 3.59). Smart continuous improvement is strongly predicted by AI, big data, and lean (R² = 0.582), with lean as the dominant driver (β = 0.36). Smart continuous improvement in turn explains 41.6 percent of the variance in zero defect performance (β = 0.65), and the full model combining all capabilities plus smart continuous improvement explains 53.8 percent of zero defect variance, indicating partial mediation. These results imply that lean and continuous improvement should be treated as the backbone of zero defect programs, with AI and big data designed explicitly to enhance data driven problem solving and defect prevention routines rather than operating as isolated digital initiatives.
Copyright (c) 2023 Md Foysal Hossain, Aditya Dhanekula (Author)
DIGITAL TWIN-DRIVEN THERMODYNAMIC AND FLUID DYNAMIC SIMULATION FOR EXERGY EFFICIENCY IN INDUSTRIAL POWER SYSTEMS
This study addresses the persistent problem that many industrial power systems still exhibit low exergy efficiency even after decades of energy efficiency programs, largely because second law metrics are not embedded in day-to-day digital decision tools. The purpose is to examine how digital twin driven thermodynamic and fluid dynamic simulation, deployed through cloud and enterprise platforms, relates to exergy efficiency at plant level. Using a quantitative, cross sectional, case-based design, data were collected via structured questionnaires from 214 engineers, operators and managers in steam, combined cycle and cogeneration power plants that have implemented digital twin solutions. Key variables include digital twin adoption, simulation sophistication, data quality, organizational support and perceived exergy efficiency, measured on five-point Likert scales. Descriptive statistics and reliability analysis (Cronbach’s alpha 0.84 to 0.89) were followed by correlation and multiple regression, including interaction terms. The core model explained 51.9 percent of the variance in exergy efficiency (R² = 0.519), with simulation sophistication (β = 0.34, p < .001) and digital twin adoption (β = 0.27, p < .001) as the strongest predictors; data quality (β = 0.18, p = .002) and organizational support (β = 0.16, p = .006) were also significant. When moderation effects were added, explained variance rose to 57.3 percent (R² = 0.573), and interactions between digital twin adoption and data quality (β = 0.15, p = .004) and between adoption and organizational support (β = 0.12, p = .015) were significant, showing that high quality data and strong management backing amplify exergy gains. The findings imply that exergy-oriented improvements are maximized when plants jointly invest in high fidelity digital twin models, robust data governance and sustained organizational support for simulation-based decision making in industrial power systems.
Thermodynamic and Fluid Dynamic Simulation
Copyright (c) 2023 S. M. Habibullah, Muhammad Mohiul Islam (Author)
MACHINE LEARNING APPROACHES FOR OPTIMIZATION OF LUBRICANT PERFORMANCE AND RELIABILITY IN COMPLEX MECHANICAL AND MANUFACTURING SYSTEMS
This quantitative, cross-sectional, case-based study examines the role of machine learning (ML) approaches in enhancing lubricant condition monitoring and, in turn, optimizing lubricant performance and improving equipment reliability across complex mechanical and manufacturing systems. The research is motivated by the persistent underutilization of lubricant-related data in predictive maintenance programs, even as modern industrial assets increasingly incorporate dense sensor arrays capable of generating high-frequency tribological, thermal, and chemical measurements. To address this gap, the study surveyed 214 professionals responsible for maintenance and reliability management in enterprise-scale manufacturing plants operating high-duty rotating and sliding equipment, generating 204 complete, analyzable cases measured on a five-point Likert scale. The conceptual framework was structured around several core latent variables: ML-driven lubrication adoption, lubricant performance, system reliability, data quality, technical readiness, and organizational readiness. Descriptive statistics indicated that while respondents reported only moderate levels of ML adoption (M = 3.21, SD = 0.74), the plants demonstrated comparatively high lubricant performance (M = 3.68, SD = 0.69) and system reliability (M = 3.59, SD = 0.71), suggesting that lubrication improvements and reliability outcomes are already being pursued through traditional means, with ML technologies representing an emergent, rather than fully mature, complement to existing programs. All multi-item measurement scales showed strong internal consistency, with Cronbach’s alpha values ranging from .81 to .89, confirming the reliability of the constructs and supporting their suitability for subsequent correlation and regression analyses. Pearson correlation coefficients revealed that ML adoption was positively and moderately associated with both lubricant performance (r = .52, p < .001) and system reliability (r = .48, p < .001), providing initial empirical support for the theorized relationships. These correlations suggest that plants beginning to integrate ML-enabled lubrication analytics are already experiencing measurable operational benefits, potentially due to improved detection of lubricant degradation, earlier identification of contamination events, or more precise adjustment of lubrication intervals. Multiple regression results further clarified these relationships, demonstrating that the proposed model explained 41.8% of the variance in lubricant performance and 49.2% of the variance in system reliability. ML adoption emerged as a significant predictor across models (β up to .38, p < .001), but data quality and organizational readiness also showed strong predictive influence, highlighting the critical interdependencies between algorithmic tools, underlying data infrastructures, and human/organizational processes.
Copyright (c) 2021 Zobayer Eusufzai (Author)
BIG DATA AND ENGINEERING ANALYTICS PIPELINES FOR SMART MANUFACTURING: ENHANCING EFFICIENCY, QUALITY, AND PREDICTIVE MAINTENANCE
This study addresses the practical problem that many smart manufacturing firms deploy isolated analytics tools without coherent big data and engineering analytics pipelines, which limits gains in efficiency, quality, and predictive maintenance. The purpose is to empirically examine how the maturity of such pipelines influences three core performance dimensions in smart factories. A quantitative, cross sectional, case-based survey design was applied to 150 respondents from cloud enabled smart manufacturing enterprises, using Likert 5-point scales to measure analytics pipeline maturity, efficiency performance, quality performance, and predictive maintenance effectiveness, with firm size, industry segment, and automation level as controls. Descriptive analysis shows moderate to high maturity (PIPE mean 3.84, SD 0.62) and positive perceived outcomes for efficiency (mean 3.91), quality (3.77), and predictive maintenance (3.69), with strong reliability for all scales (Cronbach’s alpha 0.86 to 0.89). Correlation analysis indicates significant positive associations between pipeline maturity and efficiency (r 0.52), quality (r 0.47), and predictive maintenance (r 0.58, p less than .001). Multiple regression confirms that pipeline maturity is a significant predictor of efficiency (β 0.41, R² 0.34), quality (β 0.37, R² 0.29), and predictive maintenance (β 0.48, R² 0.41) after controls, while mediation tests show that predictive maintenance partially mediates the pipeline–efficiency relationship, increasing explained variance in efficiency to 0.43. These results imply that managers should treat engineering analytics pipelines as a strategic, plant level capability, prioritizing end to end data integration, high quality sensor and enterprise data, and predictive maintenance analytics to unlock scalable improvements in throughput, defect reduction, and downtime control.
Copyright (c) 2022 M.A. Rony, Ashraful Islam (Author)
POST-GDPR DIGITAL COMPLIANCE IN MULTINATIONAL ORGANIZATIONS: BRIDGING LEGAL OBLIGATIONS WITH CYBERSECURITY GOVERNANCE
This study examined how multinational corporations operationalize the requirements of the General Data Protection Regulation (GDPR) within cybersecurity governance frameworks to achieve measurable compliance performance. Using a quantitative, cross-sectional research design, the study analyzed the interrelationships among compliance maturity, control effectiveness, governance efficiency, and risk mitigation across approximately 400 multinational subsidiaries operating in at least three international jurisdictions. Data were collected through structured surveys and archival compliance records and analyzed using confirmatory factor analysis and structural equation modeling (SEM). Reliability and validity were confirmed through high Cronbach’s alpha, composite reliability, and average variance extracted (AVE) values, ensuring methodological rigor and construct accuracy. Descriptive analysis revealed that organizations generally demonstrated high compliance maturity and governance efficiency, though variation persisted in control execution and risk mitigation outcomes. Correlation and regression analyses indicated strong, positive, and statistically significant relationships among all constructs. Compliance maturity emerged as a significant predictor of both governance efficiency and control effectiveness, while governance efficiency and control effectiveness significantly influenced risk mitigation performance. Mediation analysis confirmed that governance efficiency partially mediated the link between compliance maturity and risk mitigation, establishing governance as the conduit through which compliance maturity translates into improved cybersecurity outcomes. Moderation analysis showed that cross-border operational complexity weakened the impact of control effectiveness on risk mitigation, highlighting the challenges of maintaining consistent compliance performance across diverse regulatory environments. The structural model achieved strong goodness-of-fit indices, validating the hypothesized relationships and confirming that integrated compliance and governance systems enhance cybersecurity resilience. Overall, the findings demonstrated that post-GDPR digital compliance functions as a quantifiable governance mechanism—linking legal adherence, operational control, and risk reduction into a unified accountability framework. The study recommends that multinational organizations institutionalize compliance as a continuous governance process supported by data analytics, automated monitoring, cross-functional oversight, and jurisdiction-specific adaptation to sustain measurable regulatory alignment and long-term digital trust.
Copyright (c) 2021 Md. Omar Faruq, Md Harun-Or-Rashid Mollah (Author)
BLOCKCHAIN-ORCHESTRATED CYBER-PHYSICAL SUPPLY CHAIN NETWORKS FOR MANUFACTURING RESILIENCE
This study examines how blockchain-orchestrated cyber-physical supply chains (CPSCs) contribute to manufacturing resilience by translating blockchain capabilities into measurable antecedents and testing their relationships with visibility, agility, robustness, and recovery outcomes. Drawing on dynamic capabilities and information-processing theories, the research conceptualizes blockchain orchestration as a multi-dimensional capability encompassing traceability, smart-contract automation, IoT–ledger interoperability, and governance quality. These dimensions collectively function as coordination mechanisms that enhance supply chain visibility and agility, thereby reinforcing resilience under environmental turbulence. Using a quantitative, cross-sectional, multi–case design embedded in active manufacturing consortia, the study surveyed 204 firms and plants across OEMs, suppliers, and logistics partners engaged in production-grade blockchain implementations. Measurement scales were validated for reliability, convergent, and discriminant validity, and hierarchical regression models were employed to test direct, mediated, and moderated effects, with robustness checks incorporating fixed effects, alternative indices, and bootstrap estimation. Results indicate that traceability, interoperability, and governance quality significantly predict visibility; smart-contract automation and visibility predict agility; and together, these coordination capabilities explain variance in robustness and a composite resilience index. The agility–resilience relationship is found to intensify under greater environmental turbulence, confirming agility’s contingent value in volatile contexts. Mediation tests reveal that visibility partially transmits the effects of traceability and interoperability to agility, highlighting its role as a keystone coordination capability. Collectively, the findings provide the first empirically validated framework linking blockchain orchestration capabilities to measurable resilience outcomes in CPSCs. Conceptually, the research reframes blockchain from a technological artifact to a configurable coordination layer that improves inter-firm information integrity, synchronization, and adaptive performance. Practically, the study offers a roadmap for manufacturers: invest first in traceability and interoperability, then extend to smart-contract automation under strong governance. This evidence-based approach positions blockchain orchestration as a foundational capability for building auditable, agile, and resilient digital supply networks in manufacturing ecosystems.
Copyright (c) 2023 Md Sanjid Khan, Sudipto Roy (Author)
CLOUD-NATIVE DATA PIPELINES FOR SCALABLE AUDIO ANALYTICS AND SECURE ENTERPRISE APPLICATIONS
This study responds to a critical gap in the empirical understanding of how cloud-native architectural capabilities directly and indirectly contribute to analytics performance outcomes and enterprise business value in production-scale audio analytics environments. While industry discourse frequently asserts that cloud-native maturity enhances pipeline efficiency, resilience, and innovation velocity, systematic evidence quantifying these relationships—particularly in the context of audio data pipelines with stringent real-time processing, compliance, and observability requirements—remains limited. The central purpose is to estimate both the individual and joint effects of cloud-native maturity, pipeline automation and observability capabilities, and security and data governance frameworks on analytics performance and downstream business outcomes, reflecting the hypothesis that technical maturity and organizational governance jointly determine enterprise readiness for value extraction from audio intelligence workflows. The study employs a quantitative, cross-sectional design using a case-based survey administered across six enterprise contexts representing cloud-first and hybrid-cloud environments. A total of 198 role-verified practitioners including DevOps engineers, data architects, product leads, and security officers—from multiple industries such as telecommunications, media, healthcare, and finance participated in the study. The analysis plan follows a rigorous sequence beginning with descriptive statistics to characterize the maturity distribution of participating organizations, followed by reliability and validity assessments using Cronbach’s alpha and confirmatory factor analysis. Correlation matrices establish preliminary relationships among constructs, while hierarchical multiple regression models test theoretical expectations regarding the incremental explanatory power of each architectural and operational domain. Moderation and mediation effects are explored using PROCESS-based algorithms and structural estimation logic to evaluate whether cloud-native maturity moderates the impact of automation and observability on performance, and whether analytics performance mediates the path to business value. Robustness checks include cluster-robust standard errors to account for case-level dependencies and mixed-effects modeling to re-estimate coefficients under alternative assumptions of nested hierarchies. The findings reveal a clear pattern: automation and observability capabilities demonstrate the strongest unique association with analytics performance, suggesting that operational excellence in pipeline management yields direct gains in processing quality and reliability. The performance-to-value pathway is the dominant mechanism through which technical capabilities generate strategic benefits, affirming the mediating role of analytics effectiveness.
Copyright (c) 2024 Zamal Haider Shish, Sai Praveen Kudapa (Author)
A QUANTITATIVE ANALYSIS OF ARTIFICIAL INTELLIGENCE IN FINANCIAL RISK MANAGEMENT, PREDICTIVE FORECASTING, AND INTERNATIONAL APPLICATIONS
Financial institutions face a clear problem: translating artificial intelligence capability into measurable improvements in risk control and forecasting accuracy across heterogeneous regulatory contexts. The purpose of this study is to quantify those links. Using a quantitative cross-sectional, case-based design, we analyze 360 cloud-enabled enterprise cases spanning banks, insurers, non-bank financial institutions, and fintechs in 12 countries. A scoping review of 46 peer-reviewed studies informed construct design and hypotheses. Key variables include AI Maturity, Predictive-Use Intensity, and Governance or Risk Culture, with outcomes covering credit-loss ratio, non-performing loan ratio, Value-at-Risk exceptions, and business planning errors such as revenue and liquidity MAPE. The analysis plan combines harmonized descriptives and correlations with OLS for continuous outcomes, negative binomial models for over-dispersed counts, clustered robust standard errors by country, moderation by national digital readiness, and extensive robustness checks including leave-one-country-out and alternative estimators. Headline findings show that higher AI Maturity is associated with lower credit losses and fewer VaR exceptions, while greater Predictive-Use Intensity is associated with materially lower forecasting errors; effects are stronger in digitally ready environments and governance complements but does not substitute for maturity. Implications for practice are to prioritize data lineage, deployment automation, and monitoring, scale predictive use across risk and FP&A processes, embed explainability and subgroup calibration, and align controls to the strictest-applicable regulatory standard so gains travel across jurisdictions.
Copyright (c) 2022 Atika Dola, Fariha Noor Nitu Fariha Noor Nitu (Author)
ADVANCING THREAT DETECTION THROUGH ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING ENHANCED CYBERSECURITY AUDITS
The increasing complexity of cyber threats and the intensification of regulatory demands have elevated cybersecurity auditing into a strategic imperative for organizations across sectors. Traditional audits, often reliant on manual verification and rule-based models, have shown limitations in efficiency, scalability, and accuracy. This study explores how artificial intelligence (AI) and machine learning (ML) transform cybersecurity auditing by enhancing compliance automation, threat detection, auditor trust, continuous monitoring, and sector-specific assurance in finance and healthcare. Seven hypotheses were developed, grounded in computational learning theory, risk governance, and explainable AI frameworks, to examine both the direct and moderating effects of these technologies on audit effectiveness. A quantitative cross-sectional survey was conducted with 245 professionals, including auditors, compliance officers, IT managers, and security executives from finance, healthcare, energy, and government organizations. Data were analyzed using multiple regression and structural equation modeling to assess hypothesized relationships. The findings provide strong empirical support: AI-based compliance automation significantly improved audit efficiency (β = 0.42, p < .001), reducing cycle times and minimizing human error; ML-driven models enhanced threat detection accuracy (β = 0.47, p < .001), lowering false positives and identifying complex anomalies; and explainable AI features increased auditor trust in automated outcomes (β = 0.36, p < .001), particularly among less experienced professionals. Continuous auditing enabled by AI and robotic process automation was strongly associated with reduced organizational risk exposure (β = −0.51, p < .001), demonstrating tangible governance benefits. Domain-specific analysis confirmed that AI integration improved fraud detection rates in financial services (β = 0.44, p < .001) and strengthened HIPAA compliance in healthcare (β = 0.39, p < .001). Furthermore, international standards such as ISO/IEC 27001 and the NIST Cybersecurity Framework moderated these relationships, amplifying the positive effects of AI adoption on audit effectiveness (β = 0.55, p < .001). These results highlight that AI and ML are not supplementary tools but central mechanisms that redefine the scope, accuracy, and reliability of cybersecurity auditing. The study contributes theoretically by extending the integration of computational intelligence with governance frameworks and contributes practically by offering evidence-based insights for auditors, regulators, and organizational leaders.
Copyright (c) 2025 Debashish Goswami (Author)
MACHINE LEARNING APPLICATIONS IN RENEWABLE ENERGY: PREDICTIVE ANALYTICS FOR SOLAR CELL PERFORMANCE OPTIMIZATION AND ENERGY YIELD FORECASTING
This systematic review synthesizes contemporary evidence on machine-learning (ML) applications for solar photovoltaic (PV) performance optimization and energy-yield forecasting, spanning algorithms, data infrastructures, evaluation practice, and operational integration. Following PRISMA guidelines, we screened multidisciplinary databases and included 214 empirical studies for qualitative synthesis. Findings reveal a consistent accuracy hierarchy: tuned tree-based ensembles are the most dependable and computationally economical for day-ahead, tabular mappings of numerical weather prediction and plant telemetry; deep neural architectures (e.g., CNN/LSTM and hybrids) dominate minute-to-hour nowcasting when inputs are image- or sequence-rich; and physics–ML hybrids improve robustness and physical plausibility under regime shifts or sparse data. Cross-regional validation exposes systematic optimism in single-site splits; region-out testing typically increases error, while transfer learning and domain adaptation halve that penalty in many cases. Data quality emerges as the performance ceiling: standards-aligned sensing, explicit soiling treatment, synchronized timestamps, and streaming feature engineering yield error reductions comparable to algorithmic gains. IoT and big-data stacks—edge inference for sub-minute latency paired with cloud-based training, monitoring, and drift management—prove critical for real-time operation. Beyond forecasting, image- and I–V–based diagnostics achieve high scores for fault detection, and sequence-aware prognostics support remaining-useful-life estimation. Explainability layers (e.g., attribution or saliency) facilitate adoption without sacrificing accuracy, especially when coupled with physics-guided features and probabilistic outputs for grid dispatch and storage control. Overall, durable value arises from aligning horizon-appropriate models with disciplined data pipelines, climate-aware evaluation, and production-grade MLOps; future progress hinges on broader geographic coverage, open benchmarks, advances in transfer/physics-informed learning, and governance that ensures transparency, security, and market interoperability.
Copyright (c) 2025 Abdus Salam Howlader (Author)
BLOCKCHAIN-ENABLED BI FOR HR AND PAYROLL SYSTEMS: SECURING SENSITIVE WORKFORCE DATA
This study systematically reviewed the integration of blockchain and business intelligence (BI) within human resource information systems (HRIS) and payroll platforms to assess their potential in securing sensitive workforce data while enhancing organizational decision-making. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, the review identified, screened, and synthesized 218 relevant studies published between 2010 and 2022 across multiple disciplines, including information systems, management, computer science, and data governance. After rigorous inclusion and exclusion procedures, the final evidence base encompassed 134 peer-reviewed journal articles, 52 conference proceedings, and 32 gray literature sources, representing a cumulative citation impact exceeding 37,000. The findings revealed a clear evolution of HR and payroll systems from traditional administrative recordkeeping mechanisms into strategic infrastructures embedded with advanced predictive analytics and governance functions. Blockchain consistently emerged as a transformative security layer by providing immutability, distributed consensus, and smart contract functionalities, which collectively mitigate risks associated with payroll fraud, insider manipulation, and reconciliation errors. In parallel, BI tools were identified as critical enablers of workforce planning, compensation benchmarking, pay equity analysis, compliance auditing, and predictive modeling of employee retention and turnover trends. Sector-specific applications highlighted the adaptability of blockchain-BI frameworks. In petroleum and power industries, they facilitated hazard pay calculations and union negotiations; in public institutions, they improved compliance monitoring and reporting accuracy; and in multinational corporations, they enabled cross-border payroll harmonization, transparency in expatriate compensation, and streamlined tax compliance. The synthesis also demonstrated that convergence with emerging technologies such as Internet of Things (IoT) devices, digital twins, and reliability engineering principles was driving the creation of cyber-physical HR systems. These next-generation infrastructures enabled automation of attendance verification, workforce simulations, predictive compensation forecasting, and anomaly detection in wage disbursement. Collectively, this systematic review positions blockchain-BI integration within HRIS and payroll as a critical yet evolving paradigm for securing sensitive workforce data, advancing organizational intelligence, and shaping the future of data-driven human resource management.
Copyright (c) 2022 Omar Muhammad Faruk, Md.Kamrul Khan (Author)
AI DRIVEN PREDICTIVE MAINTENANCE IN PETROLEUM AND POWER SYSTEMS USING RANDOM FOREST REGRESSION MODEL FOR RELIABILITY ENGINEERING FRAMEWORK
This study systematically reviews the application of artificial intelligence (AI)-driven predictive maintenance in petroleum and power systems, with a focus on Random Forest regression as a reliability engineering tool. Predictive maintenance, defined as the integration of real-time monitoring with analytical forecasting, has become essential for minimizing downtime, reducing costs, and improving safety in energy infrastructures. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, 92 peer-reviewed studies published between 2000 and 2024 were identified and analyzed across major databases. The review synthesized literature on conceptual frameworks, including distinctions between corrective, preventive, condition-based, and predictive maintenance, as well as core reliability metrics such as mean time to failure (MTTF), mean time between failures (MTBF), and remaining useful life (RUL). The findings demonstrated that Random Forest regression consistently balanced predictive accuracy, robustness, and interpretability compared with other machine learning methods, including neural networks, support vector machines, and gradient boosting. Applications in petroleum systems emphasized drilling reliability, well integrity, pipeline monitoring, and refinery optimization, while power system studies focused on turbine reliability, transformer fault prediction, renewable energy components, and smart grid stability. The integration of predictive maintenance with Internet of Things (IoT) sensors, digital twins, and cloud-based platforms was identified as a key enabler of real-time reliability analytics. However, persistent challenges remain in terms of scalability, interpretability, and sector-specific customization. This review contributes by consolidating current evidence, identifying research gaps, and offering practical recommendations for enhancing reliability and sustainability in petroleum and power industries.
Copyright (c) 2025 Sabuj Kumar Shil (Author)
GOLD NANOPARTICLE–MEDIATED PLASMONIC BLOCK COPOLYMERS: DESIGN, SYNTHESIS, AND APPLICATIONS IN SMART DRUG DELIVERY
The development of advanced drug delivery systems has become a central focus in biomedical engineering and pharmaceutical sciences, particularly with the integration of nanotechnology into polymeric platforms. In this study, we present the design, synthesis, and application of an asymmetric diblock copolymer mediated by plasmonic gold nanoparticles for controlled and stimuli-responsive drug delivery. The system leverages the unique physicochemical properties of gold nanoparticles, including surface plasmon resonance, biocompatibility, and tunable optical activity, in conjunction with the self-assembly behavior of amphiphilic block copolymers. The copolymer is composed of hydrophilic and hydrophobic segments, strategically engineered to facilitate micelle formation in aqueous environments, where the hydrophobic core serves as a reservoir for poorly soluble therapeutic molecules. The incorporation of plasmonic gold nanoparticles into the polymer backbone introduces multifunctional responsiveness to external stimuli. Specifically, near-infrared (NIR) light irradiation induces localized heating via plasmonic resonance, resulting in the destabilization and disruption of the micellar architecture. This process enables the controlled release of hydrophobic drugs encapsulated in the micelle interior. The synthetic design of the diblock copolymer was optimized to balance stability in circulation with responsiveness upon exposure to external triggers. Structural characterization confirmed successful conjugation of the polymeric segments with gold nanoparticles, while self-assembly studies revealed stable micelle formation under physiological conditions. Drug encapsulation efficiency was evaluated using hydrophobic model compounds, and release kinetics demonstrated significant responsiveness to NIR irradiation, confirming the role of plasmonic resonance in micelle destabilization. Preliminary cytocompatibility studies suggest that the hybrid system maintains biocompatibility, underscoring its potential in translational applications. The findings lay the groundwork for further exploration of plasmonic block copolymers in areas such as targeted cancer therapy, photothermal treatment, and multimodal therapeutic strategies. Future investigations may focus on in vivo pharmacokinetics, biodistribution, and therapeutic efficacy to validate the clinical potential of this system.
Copyright (c) 2024 Dipongkar Ray Sobuj, Tamanna Rashid, Saiful Islam Arif, Shraboni Ghosh (Author)
PROGNOSTIC MODELING FOR HEPATIC DISORDERS: A PARADIGM OF EQUILIBRATED AND GENERALIZED MACHINE LEARNING METHODOLOGIES
In human physiology, the liver is a vital organ responsible for performing critical functions such as bile production, bilirubin excretion, metabolism of proteins and carbohydrates, enzyme activation, glycogen storage, and plasma protein synthesis. However, it is highly susceptible to damage due to alcohol consumption, certain medications, and poor dietary habits. Traditional diagnostic methods for liver disorders, including blood tests and imaging, are time-consuming and costly, often delaying crucial treatment. This study introduces a machine learning-based prognostic framework to enhance the speed and accuracy of liver disease diagnosis. The proposed approach integrates advanced algorithms, including Random Forest, Gradient Boosting, XGBoost, and LightGBM, combined with an ensemble voting method to leverage their complementary strengths. Preprocessing techniques such as Principal Component Analysis (PCA) for dimensionality reduction and Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance were employed to refine the dataset. Evaluation metrics like precision, recall, F1-score, accuracy, and ROC-AUC revealed the ensemble model’s superior performance, achieving the highest accuracy of 98% and a ROC-AUC of 0.9963, significantly outperforming individual models. This study offers a scalable and cost-effective solution that reduces diagnostic time and improves predictive reliability. The framework provides significant advantages for medical applications, serving as a decision support tool to aid healthcare professionals in timely and accurate liver disorder diagnosis, particularly in resource-limited settings.
Copyright (c) 2025 Md Abubakkar, Fowzy Alhussin Alsaud, Tanjim Nahar Dolon, Kazi Shaharair Sharif (Author)
FEDERATED LEARNING FOR PRIVACY-PRESERVING HEALTHCARE DATA SHARING: ENABLING GLOBAL AI COLLABORATION
This study provides a comprehensive systematic review of federated learning as a framework for privacy-preserving healthcare data sharing and its potential to enable global artificial intelligence collaboration. In total, 124 peer-reviewed articles were examined following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure transparency, rigor, and reproducibility. The review highlights how federated learning has evolved from conceptual discussions to practical applications across multiple healthcare domains, including medical imaging, electronic health records, biosignals, and genomic analysis. Key findings indicate that federated architectures, particularly server–client models, have become the dominant deployment strategy, while peer-to-peer approaches are gaining attention for their resilience and decentralization. Privacy-preserving mechanisms—such as differential privacy, secure aggregation, and cryptographic computation—emerged as central to ensuring compliance with regulatory and ethical standards, with adaptive strategies allowing for an effective balance between confidentiality and model utility. Evidence from multi-institutional collaborations shows that federated learning not only improves predictive performance but also enhances inclusivity, enabling smaller or resource-limited institutions to contribute meaningfully without relinquishing data ownership. At the same time, empirical studies identified adversarial risks such as gradient inversion, membership inference, and poisoning attacks, underscoring the necessity for layered safeguards and strong governance structures. Collectively, the findings demonstrate that federated learning is more than a technical innovation; it represents a socio-technical paradigm that integrates privacy, equity, and collaboration into the development of global healthcare AI. This review positions federated learning as a cornerstone for building secure, ethical, and scalable artificial intelligence systems that address the dual imperatives of advancing medical innovation while safeguarding patient confidentiality.
Federated Learning In Healthcare Systems
Global Artificial Intelligence Collaboration
Copyright (c) 2025 Sai Srinivas Matta, Manish Bolli (Author)
DATA-DRIVEN MOLECULAR DOCKING AND COMPUTATIONAL ANALYSIS OF ANTI-HYPERGLYCEMIC COMPOUNDS FROM MANGIFERA INDICA
This study presents a comprehensive systematic review of data-driven molecular docking and computational analysis of anti-hyperglycaemic compounds derived from Mangifera indica, integrating computational predictions with experimental evidence to evaluate their therapeutic potential in the management of type 2 diabetes mellitus. A total of 68 research articles were reviewed, encompassing docking simulations, in vitro enzyme assays, in vivo animal models, and limited clinical studies. The analysis revealed that mango-derived phytochemicals such as Mangifera, quercetin, gallic acid, and catechins consistently demonstrate strong binding affinities with key diabetic targets including alpha-glucosidase, alpha-amylase, dipeptidyl peptidase-4, protein tyrosine phosphatase 1B, glucokinase, AMP-activated protein kinase, and GLUT4 transporters. These interactions translate into multiple pharmacological effects, including inhibition of carbohydrate-digesting enzymes, preservation of incretin hormones, enhancement of insulin signalling, and facilitation of cellular glucose uptake. The evidence highlights the multi-target and synergistic nature of mango phytochemicals, underscoring their relevance for addressing the complex pathophysiology of type 2 diabetes. Convergence between docking predictions and experimental validations reinforces the reliability of computational methods as effective tools for natural product research. However, despite strong preclinical evidence, the review identified significant gaps, including limited human clinical data, variability in computational methodologies, and insufficient pharmacokinetic evaluations. The cumulative findings position Mangifera indica as both an ethnopharmacological resource and a scientifically validated candidate for modern therapeutic development. By systematically synthesizing insights from 68 articles, this review establishes a robust foundation for future research aimed at standardizing computational pipelines, conducting pharmacokinetic and toxicity studies, and advancing mango-derived compounds toward clinical application as safe, effective, and accessible antidiabetic interventions.
Copyright (c) 2023 Sadia Tasnim, Mansura Akter Enni, Md Abdul Ahad Maraj (Author)
DATA-DRIVEN LIFECYCLE ASSESSMENT OF SMART INFRASTRUCTURE COMPONENTS IN RAIL PROJECTS
This study provides a systematic review of the integration of lifecycle assessment (LCA) with smart infrastructure and data-driven methodologies in rail projects, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. A total of 134 peer-reviewed studies, published between 2000 and 2023, were systematically identified, screened, and synthesized to ensure methodological rigor and transparency. The review covered literature drawn from environmental science, civil engineering, data science, and governance domains, generating an evidence base supported by more than 25,000 cumulative citations. The findings revealed that rail construction remains the most resource- and energy-intensive stage, with concrete, steel, and ballast dominating embodied emissions. Long-term maintenance, replacement cycles, and end-of-life processes were shown to equal or even exceed construction burdens, underscoring the necessity of recycling strategies and circular economy practices. Electrification and renewable energy integration emerged as decisive factors for operational sustainability, consistently reducing emissions when coupled with energy-efficient technologies such as regenerative braking and lightweight rolling stock. A particularly significant contribution of recent studies was the integration of digital tools—such as IoT sensors, predictive analytics, BIM, and digital twins—into LCA frameworks, transforming sustainability assessments from static, retrospective analyses into dynamic, adaptive systems responsive to real-time performance data. Governance and institutional capacity were also identified as critical, with board-level oversight, compliance frameworks, and international standards shaping the effectiveness of LCA adoption across regions. Comparative evidence confirmed that rail consistently outperforms road and air transport in lifecycle sustainability, particularly under electrification and renewable integration, while cross-border case studies highlighted the importance of shared platforms and regulatory harmonization. Collectively, this review demonstrates that data-driven LCA is not only a methodological advancement but also a conceptual framework that links environmental performance, digital innovation, and governance capacity, establishing it as a foundation for sustainable and resilient rail infrastructure in the digital age.
Copyright (c) 2023 Md. Sakib Hasan Hriday (Author)
DEPLOYMENT AND PERFORMANCE EVALUATION OF HYBRID MACHINE LEARNING MODELS FOR STOCK PRICE FORECASTING AND RISK PREDICTION IN VOLATILE MARKETS
This study investigates how hybrid machine learning systems can be implemented and deployed to deliver reliable stock price forecasting and risk prediction in volatile, internationally integrated markets. Using a PRISMA protocol, we reviewed 120 peer-reviewed studies with deployment-relevant detail, harmonized their metrics, and synthesized evidence across two layers: predictive performance and MLOps operations. The analysis shows that hybrids consistently convert modest single-digit reductions in point error into materially better probabilistic calibration, with tighter Value-at-Risk and Expected Shortfall coverage that holds up under walk-forward evaluation and during high-volatility regimes. Design patterns that travel well from lab to production include combining a decomposable statistical baseline with a tabular learner and a sequence or attention model, then learning dynamic, regime-aware weights on rolling residuals. On the engineering side, studies that report model registries, CI or CD gates, canary or shadow rollouts, drift and exceedance monitoring, and rollback playbooks exhibit smaller backtest-to-live gaps and lower reversal rates, highlighting that disciplined data contracts and promotion controls function as performance multipliers rather than overhead. Measurement choices further amplify deployability, as realized-volatility and lightweight range-based estimators improve distributional sharpness at low computational cost, while portable microstructure features strengthen short-horizon direction without violating latency budgets. Overall, the evidence supports a practical blueprint that integrates feature stores, reproducible pipelines, dynamic hybridization, and risk-aware monitoring to produce forecasting and risk services that are auditable, explainable, and resilient under market stress, turning incremental accuracy into dependable tail behavior suitable for real-world deployment.
Copyright (c) 2025 Md Shah Ali Dolon (Author)
EMPIRICAL ANALYSIS OF THE ECONOMIC IMPACT OF PRIVATE ECONOMIC ZONES ON REGIONAL GDP GROWTH: A DATA-DRIVEN CASE STUDY OF SIRAJGANJ ECONOMIC ZONE
This study investigates the economic impact of private economic zones (PEZs) on regional gross domestic product (GDP) growth, with a particular focus on the Sirajganj Economic Zone in Bangladesh. Employing a systematic approach guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, a total of 35 peer-reviewed articles and institutional reports published between 1995 and 2025 were reviewed. The analysis synthesizes both domestic and international evidence to uncover patterns, mechanisms, and comparative insights into how PEZs influence regional economic performance and structural transformation. Findings demonstrate that PEZs contribute substantially to employment generation, industrial output expansion, and export competitiveness, thereby stimulating measurable GDP growth at the regional level. These effects often extend beyond the designated zones through supplier development, labor market spillovers, technological transfer, and infrastructure multipliers that enhance broader regional connectivity and productivity. The comparative analysis between public and private zone models reveals distinct performance characteristics: private zones exhibit greater efficiency in implementation, stronger investor confidence, and a broader mix of domestic and foreign direct investment, whereas public zones emphasize compliance, long-term governance stability, and alignment with national industrial policies. The Sirajganj Economic Zone is highlighted as a case study of how an inland, privately managed zone can deliver tangible economic benefits when supported by targeted infrastructure investments, policy facilitation, and integration into supply chain networks. It demonstrates that the success of PEZs depends not only on investment attraction but also on ensuring backward and forward linkages, labor market adaptability, and complementary infrastructure. Ultimately, this review positions private economic zones as pivotal drivers of regional economic diversification and competitiveness, while highlighting the critical role of coordinated governance, transport corridors, and supply chain integration in maximizing their developmental impact.
Copyright (c) 2022 MKA Shahinoor Rahman Jahid (Author)
ADVANCING AI IN MARKETING THROUGH CROSS BORDER INTEGRATION ETHICAL CONSIDERATIONS AND POLICY IMPLICATIONS
This study critically examines the advancement of artificial intelligence (AI) in marketing through the lens of cross-border integration, focusing on ethical considerations and policy implications. With AI-driven tools increasingly shaping global marketing strategies—ranging from personalization engines and predictive analytics to chatbots and real-time bidding systems—the ethical, cultural, and regulatory dimensions of these technologies have become more significant than ever. Drawing upon the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this review systematically analyzed a total of 126 peer-reviewed scholarly articles and 23 international policy reports published between 2015 and 2022. The findings reveal that while AI technologies greatly enhance operational scalability and customer engagement across borders, they often fall short in addressing cultural sensitivity, linguistic accuracy, and fairness in algorithmic decision-making. Notably, issues such as algorithmic bias, emotional profiling, consent asymmetry, and data sovereignty emerged as critical concerns, especially in regions with fragmented regulatory landscapes. The study further identifies significant inconsistencies among global regulatory frameworks, including the European Union’s GDPR and AI Act, China’s PIPL, and the United States’ CCPA/CPRA, which complicate compliance and increase operational costs for multinational firms. In response, emerging governance models—such as algorithmic impact assessments, explainable AI tools, and multistakeholder frameworks—are gaining momentum but remain uneven in their global adoption. This review concludes that ethical and policy-aligned AI marketing requires not only technical sophistication but also cross-sectoral collaboration and culturally adaptive governance. As AI continues to redefine global consumer interaction, this study offers timely insights into how marketers, regulators, and technologists can collaboratively build transparent, accountable, and inclusive AI systems for cross-border marketing success.
Copyright (c) 2022 Reduanul Hasan, Mohammad Shoeb Abdullah (Author)