International Journal of Business and Economics Insights
type: channel
description: The International Journal of Business and Economics Insights (IJBEI) is a peer-reviewed, open-access academic journal dedicated to publishing high-quality research that advances understanding in the fields of business, economics, and management sciences. The journal provides a scholarly platform for the dissemination of innovative studies and evidence-based insights that shape the future of global commerce, strategic decision-making, and sustainable economic development. By bringing together academics, practitioners, and policymakers, IJBEI fosters critical dialogue across disciplines and geographies, encouraging integrative approaches to solving contemporary challenges in both theory and practice. At its core, IJBEI seeks to bridge the gap between knowledge creation and application, ensuring that published research not only contributes to scholarly advancement but also provides actionable guidance for industries, governments, and institutions. With a growing demand for timely, reliable, and globally relevant insights, the journal emphasizes inclusivity, methodological rigor, and an international perspective that reflects the complexity of today’s interconnected world.
language: en
publication name: International Journal of Business and Economics Insights
items: AI–IOT CONVERGENCE IN MODERN HEALTHCARE: A FRAMEWORK FOR PREDICTIVE, DATA-DRIVEN, AND PERSONALIZED MEDICAL SYSTEMS
items: A SYSTEMATIC REVIEW OF PREDICTIVE ANALYTICS IN MARKETING DECISION-MAKING EXPLORING AI-DRIVEN CONSUMER SEGMENTATION AND AB TESTING
items: DO BANGLADESHI FOOD AND BEVERAGE ENTREPRENEUR INTEND TO ADOPT THE ADVANCED LEVEL OF IR 4.0 TECHNOLOGY IN MANUFACTURING OPERATIONS?
items: FEDERATED LEARNING MODELS FOR PRIVACY-PRESERVING DATA SHARING AND SECURE ANALYTICS IN HEALTHCARE INDUSTRY
items: QUANTUM-RESISTANT CRYPTOGRAPHIC PROTOCOLS INTEGRATED WITH AI FOR SECURING CLOUD AND IOT ENVIRONMENTS
items: AI-DRIVEN BUSINESS INTELLIGENCE FRAMEWORK FOR PREDICTIVE DECISION-MAKING AND STRATEGIC RESOURCE OPTIMIZATION
items: QUANTITATIVE INVESTIGATION OF INFORMATION SECURITY CHALLENGES IN U.S. HEALTHCARE PAYMENT ECOSYSTEMS
items: DATA-DRIVEN APPROACHES TO ENHANCING HUMAN–MACHINE COLLABORATION IN REMOTE WORK ENVIRONMENTS
items: MECHANISMS BY WHICH AI-ENABLED CRM SYSTEMS INFLUENCE CUSTOMER RETENTION AND OVERALL BUSINESS PERFORMANCE: A SYSTEMATIC LITERATURE REVIEW OF EMPIRICAL FINDINGS
items: THE ROLE OF DATA SCIENCE IN OPTIMIZING PROJECT EFFICIENCY AND INNOVATION IN U.S. ENTERPRISES
items: THE ROLE OF AI-DRIVEN CYBER RISK ANALYTICS ON CLOUD SECURITY POSTURE MANAGEMENT IN ENTERPRISE SYSTEMS
items: A SYSTEMATIC REVIEW OF DATA-DRIVEN BUSINESS PROCESS REENGINEERING AND ITS IMPACT ON ACCURACY AND EFFICIENCY CORPORATE FINANCIAL REPORTING
items: DATA DRIVEN BUSINESS INTELLIGENCE TOOLS IN AGRIBUSINESS A FRAMEWORK FOR EVIDENCE-BASED MARKETING DECISIONS
items: INTEGRATION OF MACHINE LEARNING AND ADVANCED COMPUTING FOR OPTIMIZING RETAIL CUSTOMER ANALYTICS
items: AI-ENHANCED BUSINESS INTELLIGENCE DASHBOARDS FOR PREDICTIVE MARKET STRATEGY IN U.S. ENTERPRISES
items: OPTIMIZING THE U.S. GREEN HYDROGEN ECONOMY: AN INTEGRATED ANALYSIS OF TECHNOLOGICAL PATHWAYS, POLICY FRAMEWORKS, AND SOCIO-ECONOMIC DIMENSIONS
items: QUANTITATIVE USABILITY STUDY ON IOT-BASED HUMAN-CENTERED SECURITY INTERFACES FOR HOSPITAL CRITICAL INFRASTRUCTURE
items: THE IMPACT OF DATA-DRIVEN WEB FRAMEWORKS ON PERFORMANCE AND SCALABILITY OF U.S. ENTERPRISE APPLICATIONS
items: SMART CORRIDOR SIMULATION FOR PEDESTRIAN SAFETY: : INSIGHTS FROM VISSIM-BASED URBAN TRAFFIC MODELS
items: IT AUTOMATION AND DIGITAL TRANSFORMATION STRATEGIES FOR STRENGTHENING CRITICAL INFRASTRUCTURE RESILIENCE DURING GLOBAL CRISES
items: MODBUS/DNP3 OVER TCP/IP IMPLEMENTATION ON TMDSCNCD28388D AND ARDUINO WITH SIMULINK HMI FOR IOT-BASED CYBERSECURE ELECTRICAL SYSTEMS
items: SECURING ERP SYSTEMS: THE ROLE OF INFORMATION SECURITY ANALYSTS IN U.S. TEXTILE AND MANUFACTURING ENTERPRISES
items: EFFECT OF TEMPERATURE‐DEPENDENT CRACK CLOSURE AND CRACK BRIDGING ON LONG CRACK PROPAGATION AT ELEVATED AND CYCLIC TEMPERATURE DURING HIGH CYCLE FATIGUE
items: AI-AUGMENTED CYBERSECURITY: GRAPH NEURAL NETWORKS FOR PREDICTING NATION-STATE CYBERATTACKS
items: AI-DRIVEN BUSINESS ANALYTICS FOR COMPETITIVE ADVANTAGE IN SERVICE-ORIENTED ENTERPRISES: CUSTOMER EXPERIENCE AND EFFICIENCY
items: THE IMPACT OF DATA-DRIVEN INDUSTRIAL ENGINEERING MODELS ON EFFICIENCY AND RISK REDUCTION IN U.S. APPAREL SUPPLY CHAINS
items: A SYSTEMATIC REVIEW ON THE IMPACT OF NFPA-COMPLIANT FIRE PROTECTION SYSTEMS ON U.S. INFRASTRUCTURE RESILIENCE
items: DIGITAL ENGINEERING AND PROJECT MANAGEMENT FRAMEWORKS FOR IMPROVING SAFETY AND EFFICIENCY IN US CIVIL AND RAIL INFRASTRUCTURE
items: INNOVATION OF BIODEGRADABLE ANTIMICROBIAL FABRICS FOR SUSTAINABLE FACE MASKS PRODUCTION TO REDUCE RESPIRATORY DISEASE TRANSMISSION
items: AI-DRIVEN WATER PURIFICATION MODEL IMPLEMENTATION IN SMART CITIES: REAL-TIME SOLAR DESALINATION AND EFFLUENT TREATMENT

AI–IOT CONVERGENCE IN MODERN HEALTHCARE: A FRAMEWORK FOR PREDICTIVE, DATA-DRIVEN, AND PERSONALIZED MEDICAL SYSTEMS
type: item
description: This study addresses an implementation gap in healthcare digitalization: organizations deploy connected sensing and AI analytics yet lack case-based quantitative evidence on which AI-IoT convergence capabilities drive predictive, data-driven, and personalized care benefits. The purpose was to test a convergence framework in a quantitative, cross-sectional, case-study design within a healthcare enterprise case. A five-point Likert survey was administered to 210 stakeholders across clinical, administrative, and health IT roles. Independent variables were AI Analytics Capability (AIC), IoT Data Acquisition Capability (IDC), Interoperability and System Integration (ISI), Data Quality and Governance (DQG), Security and Privacy Assurance (SPA), Infrastructure Readiness (IR), and Clinician/User Acceptance (CUA); outcomes were Predictive Healthcare Effectiveness (PHE), Data-Driven Decision Quality (DDDQ), and Personalized Care Effectiveness (PCE). The analysis plan used descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression. Reliability supported composite scoring (α = .83–.91; PHE α = .90, DDDQ α = .88, PCE α = .91). Baseline ratings exceeded the neutral midpoint (AIC M = 3.86; PHE M = 3.77). Correlations supported positive capability-outcome links (AIC with PHE r = .54; DQG with DDDQ r = .51; p < .01). Regression models showed differentiated drivers: PHE variance explained was 46% (R² = .46), with AIC (β = .31, p < .001), IDC (β = .21, p = .002), DQG (β = .18, p = .006), and IR (β = .20, p = .003) significant; DDDQ variance explained was 44% (R² = .44), led by DQG (β = .34, p < .001) and ISI (β = .22, p = .001), with SPA (β = .16, p = .012) and IR (β = .11, p = .049) also significant; PCE variance explained was 52% (R² = .52), driven by DDDQ (β = .36, p < .001), CUA (β = .29, p < .001), and PHE (β = .19, p = .004). Implications are that healthcare enterprises should prioritize governed data and integration to raise decision quality, pair a…
subject: Articles
  topic: section
subject: AI-IOT Convergence
  topic: keywords
subject: Predictive Healthcare Effectiveness
  topic: keywords
subject: Data-Driven Decision Quality
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Clinician Acceptance
  topic: keywords
creator: Abdulla Mamun
rights: Copyright (c) 2025 Abdulla Mamun (Author)
date: 2025-12-26
publication date: 2025-12-26
starting page: 1288
ending page: 1328
doi: 10.63125/402hjt35

A SYSTEMATIC REVIEW OF PREDICTIVE ANALYTICS IN MARKETING DECISION-MAKING EXPLORING AI-DRIVEN CONSUMER SEGMENTATION AND AB TESTING
type: item
description: This systematic review examines how predictive analytics supports marketing decision-making by synthesizing research on AI-driven consumer segmentation and A/B testing strategies as complementary components of evidence-based marketing systems. Using a PRISMA-guided screening and selection workflow, the review analyzed a total of 57 papers that met predefined eligibility criteria focused on marketing-relevant predictive modeling, segmentation methods used for decision support, and experimentation designs applied to validate marketing interventions and personalization policies. Findings indicate that predictive analytics is most often operationalized as an end-to-end decision pipeline in which diverse data ecosystems—spanning first-party CRM and transaction histories, digital behavioral traces, campaign exposure logs, contextual signals, and content-based attributes are transformed through feature engineering into model-ready representations that drive targeting, retention, personalization, and pricing or promotion decisions. The reviewed studies show that segmentation has expanded beyond traditional strategic planning roles to include tactical campaign governance, analytical feature construction, and high-granularity personalization through clustering, probabilistic membership models, representation learning, and deep latent-space approaches, with interpretability and segment stability repeatedly emphasized as prerequisites for operational use. The experimentation literature positions A/B testing as the central causal validation mechanism for selecting among creative variants, interface designs, targeting rules, and algorithmic policies, while also highlighting the importance of metric governance, attribution windows, statistical power, multiple-comparison control, and operational safeguards such as ramping, logging, and auditability. Across the evidence base, the strongest contributions link prediction, segmentation, and experimentation into integrated decision cycl…
subject: Articles
  topic: section
subject: Predictive analytics
  topic: keywords
subject: AI segmentation
  topic: keywords
subject: A/B testing
  topic: keywords
subject: Marketing decisions
  topic: keywords
subject: Experimentation
  topic: keywords
creator: Rakibul Hasan
creator: Khairul Alam Talukder
rights: Copyright (c) 2023 Rakibul Hasan, Khairul Alam Talukder (Author)
date: 2023-03-30
publication date: 2023-03-30
starting page: 68
ending page: 96
doi: 10.63125/2hvfh110

DO BANGLADESHI FOOD AND BEVERAGE ENTREPRENEUR INTEND TO ADOPT THE ADVANCED LEVEL OF IR 4.0 TECHNOLOGY IN MANUFACTURING OPERATIONS?
type: item
description: The performance of SME enterprises will drive the country’s economy forward. Upgrading the technologies currently used in SMEs will enhance the manufacturing process of the food and beverage (F&B) industries. Those firms are still using traditional equipment and machinery in their F&B manufacturing process; however, they are required to adopt the next level of technologies for maximum productivity, which they cannot afford. Thus, the study focuses on the intention to adopt upgraded technologies in F&B SMEs in developing countries. A total of 230 F&B SME owners and top management were surveyed in Bangladesh. The collected data were analysed using PLS-SEM with SmartPLS software. The results showed that disaster preparedness and business continuity were significant in influencing the intention to adopt; similarly, performance expectancy, effort expectancy, and social influence were also significant in influencing the intention to adopt the next level of IR technologies. The moderating role of government support and policy has mixed effects on intention. The study is significant because disaster preparedness and business continuity plans are utilised in technology adoption and intention scenarios. For the investigation of this intention, the study adopted two perspectives: the invasion and role of disasters and hazards on SMEs through disaster preparedness and business continuity plans, and secondly, the unified theory of acceptance and usage of technology (UTAUT) for measuring the intention. The study contributed to these two theories in a new context, utilising specific constructs. The study will also contribute to policymakers in the government in developing constructive policies and effective financial and non-financial support for F&B manufacturing SMEs.
subject: Articles
  topic: section
subject: Adoption Intention
  topic: keywords
subject: Bangladesh
  topic: keywords
subject: Business Continuity
  topic: keywords
subject: Disaster Preparedness
  topic: keywords
subject: F&B Manufacturing SMEs
  topic: keywords
subject: Government Support and Policy
  topic: keywords
subject: IT technology
  topic: keywords
subject: UTAUT Model
  topic: keywords
creator: Ishraq Jerin
creator: Amer Hamzah Jantan
creator: Abu Bakar Abdul Hamid
rights: Copyright (c) 2025 Ishraq Jerin, Amer Hamzah Jantan, Abu Bakar Abdul Hamid (Author)
date: 2025-12-16
publication date: 2025-12-16
starting page: 1271
ending page: 1287
doi: 10.63125/26wcwe41

FEDERATED LEARNING MODELS FOR PRIVACY-PRESERVING DATA SHARING AND SECURE ANALYTICS IN HEALTHCARE INDUSTRY
type: item
description: This study investigated the development and evaluation of Federated Learning Models for Privacy-Preserving Data Sharing and Secure Analytics in the Healthcare Industry, presenting a comprehensive quantitative framework to balance privacy, security, and utility in distributed medical data environments. Federated learning enables multiple institutions—such as hospitals, laboratories, and research centers—to collaboratively train shared models without transferring raw patient data, thereby maintaining compliance with global privacy regulations while supporting large-scale analytics. The research compared four configurations: a centralized baseline, federated learning with secure aggregation, federated learning with secure aggregation and differential privacy, and a robustness-enhanced federated model with adaptive aggregation. Empirical analysis across imaging, electronic health record, and wearable sensor datasets demonstrated that federated configurations achieved non-inferior predictive accuracy (AUROC range: 0.917–0.931) compared with the centralized model (AUROC = 0.936) while significantly improving privacy and security indicators. The membership inference advantage declined from 42.5% in centralized settings to below 10% in privacy-enhanced federated models, confirming reduced re-identification risk. Regression and correlation analyses established statistically significant relationships between privacy budgets, encryption depth, and model performance (R² = 0.709; p < 0.001), validating measurable trade-offs between confidentiality and predictive utility. Reliability and validity testing (Cronbach’s α > 0.88) confirmed the internal consistency and reproducibility of quantitative constructs. Findings revealed that privacy-preserving federated learning can achieve secure, efficient, and equitable performance across heterogeneous healthcare institutions with minimal computational overhead (<12%). The study contributes an empirically validated, statistically rigorous…
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Privacy Preservation
  topic: keywords
subject: Secure Analytics
  topic: keywords
subject: Healthcare Data
  topic: keywords
subject: Data Sharing
  topic: keywords
creator: Tonoy Kanti Chowdhury
creator: Sai Praveen Kudapa
rights: Copyright (c) 2024 Tonoy Kanti Chowdhury, Sai Praveen Kudapa (Author)
date: 2024-12-28
publication date: 2024-12-28
starting page: 91
ending page: 133
doi: 10.63125/c2dzn006

QUANTUM-RESISTANT CRYPTOGRAPHIC PROTOCOLS INTEGRATED WITH AI FOR SECURING CLOUD AND IOT ENVIRONMENTS
type: item
description: This quantitative study investigated the performance, efficiency, and security resilience of quantum-resistant cryptographic protocols integrated with artificial intelligence (AI) across cloud and Internet of Things (IoT) environments. The research aimed to empirically assess whether AI-enhanced cryptographic systems could outperform conventional post-quantum algorithms in encryption throughput, latency, resource optimization, and security robustness. A factorial experimental design was implemented, encompassing multiple algorithmic classes—lattice-based, hash-based, code-based, and multivariate polynomial systems—under both AI-integrated and non-AI configurations. The analysis incorporated 7,200 experimental runs executed under varying workloads, environments, and simulated attack conditions. Linear mixed-effects models, correlation analysis, and reliability testing were used to validate the statistical integrity of the results. The descriptive analysis indicated that AI-augmented frameworks achieved consistently higher encryption speeds, lower decryption latency, and superior throughput-adjusted security efficiency compared to traditional post-quantum systems. Correlation analysis revealed strong positive relationships between AI detection accuracy, encryption performance, and system stability, confirming that AI optimization significantly improved operational consistency. Reliability and validity tests showed high internal consistency, with Cronbach’s alpha coefficients exceeding 0.90, and factor analysis confirmed that performance indicators loaded strongly on the intended theoretical constructs of cryptographic performance and AI adaptability. Collinearity diagnostics verified the independence of predictors, with all variance inflation factors below 2.0. Regression analysis demonstrated that AI integration was a statistically significant predictor of improved cryptographic outcomes (p < 0.001), increasing throughput efficiency by over 14% on average while reduc…
subject: Articles
  topic: section
subject: Quantum Cryptography
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: IoT
  topic: keywords
subject: Encryption
  topic: keywords
creator: Shaikat Biswas
creator: Md. Wahid Zaman Raj
rights: Copyright (c) 2024 Shaikat Biswas, Md. Wahid Zaman Raj (Author)
date: 2024-12-28
publication date: 2024-12-28
starting page: 60
ending page: 90
doi: 10.63125/dryw3b96

AI-DRIVEN BUSINESS INTELLIGENCE FRAMEWORK FOR PREDICTIVE DECISION-MAKING AND STRATEGIC RESOURCE OPTIMIZATION
type: item
description: This study investigates the role of Artificial Intelligence driven Business Intelligence (AI-BI) in enhancing predictive decision-making and optimizing strategic resource management across organizations. Using a quantitative research design, data were collected through a structured survey employing five-point Likert-scale measures to examine four key constructs: AI-BI Capability, Decision-Making Effectiveness, Strategic Resource Optimization, and Organizational Performance. The analysis, conducted through Structural Equation Modeling (SEM), revealed that AI-BI Capability significantly influences both Decision-Making Effectiveness (β = 0.71, p < 0.001) and Strategic Resource Optimization (β = 0.67, p < 0.001), while Decision-Making Effectiveness, in turn, has a strong positive impact on Organizational Performance (β = 0.63, p < 0.001). Moreover, Decision-Making Effectiveness was found to partially mediate the relationship between AI-BI Capability and Organizational Performance, suggesting that the cognitive enhancement derived from AI analytics plays a crucial role in translating technological capacity into strategic outcomes. Moderation analysis further revealed that Data Quality and Data Governance significantly strengthen these relationships, highlighting the necessity of reliable, well-managed data environments. The model exhibited high predictive power, with R² values ranging from 0.64 to 0.72 and excellent model fit indices (CFI = 0.951, TLI = 0.945, RMSEA = 0.047). The findings underscore that AI-BI is not merely an analytical tool but a strategic capability that integrates machine learning, automation, and predictive intelligence to drive performance excellence. By linking cognitive decision-making mechanisms with operational intelligence, the study contributes to the theoretical discourse on the Resource-Based View (RBV) and Dynamic Capabilities Theory, positioning AI-BI as a transformative asset that fosters organizational agility, efficiency, and sustained…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Decision Support Systems
  topic: keywords
subject: Resource Optimization
  topic: keywords
creator: Aditya Dhanekula
rights: Copyright (c) 2025 Aditya Dhanekula (Author)
date: 2025-11-01
publication date: 2025-11-01
starting page: 1238
ending page: 1270
doi: 10.63125/dkt2w457

QUANTITATIVE INVESTIGATION OF INFORMATION SECURITY CHALLENGES IN U.S. HEALTHCARE PAYMENT ECOSYSTEMS
type: item
description: This study quantitatively examines information security challenges in U.S. healthcare payment ecosystems and empirically links security control maturity, incident-response capability, and organizational context to measurable payment outcomes—resilience, fraud-loss exposure, and stakeholder trust. Integrating a systematic literature review (2005–2020) with a cross-sectional survey of 124 organizations (providers, payers, clearinghouses, and vendors), the research operationalized constructs such as threat exposure, vulnerability, control maturity, compliance posture, and incident-response capability using five-point Likert scales. Findings indicate that control maturity is the strongest predictor of payment resilience (β = .36, p < .001) and stakeholder trust (β = .28, p < .001), while higher maturity and response capability jointly reduce fraud-loss ratios (β = −.24, p < .001; β = −.17, p = .006). Mediation analysis confirms that incident-response capability partially offsets the detrimental effect of threat exposure on resilience (indirect α×β = −.04, 95% CI [−.08, −.01]). Moderation tests reveal that maturity’s payoff is amplified in larger and cloud-forward organizations and weakened in vendor-dense ecosystems, underscoring the role of automation and interorganizational governance. Complementary anomaly-detection pilots achieve practical precision (0.77) and recall (0.70), demonstrating the viability of analytics-based monitoring in reducing fraud losses. The integrated model explains 29–42% of outcome variance, supporting a socio-technical interpretation in which security maturity and rehearsed response routines form the foundation of resilient, low-loss, and high-trust payment operations. The study contributes validated constructs, a transparent measurement framework, and actionable pathways for CISOs, enterprise architects, and revenue-cycle leaders to strengthen payment-system security and reliability through institutionalized control maturity, incident prepar…
subject: Articles
  topic: section
subject: Healthcare Payments
  topic: keywords
subject: Information Security
  topic: keywords
subject: Control Maturity
  topic: keywords
subject: Incident Response
  topic: keywords
subject: HIPAA
  topic: keywords
subject: PCI DSS
  topic: keywords
creator: Mohammad Mushfequr Rahaman
creator: Sai Praveen Kudapa
rights: Copyright (c) 2022 Mohammad Mushfequr Rahaman, Sai Praveen Kudapa (Author)
date: 2022-12-15
publication date: 2022-12-15
starting page: 42
ending page: 73
doi: 10.63125/gcg0fs06

DATA-DRIVEN APPROACHES TO ENHANCING HUMAN–MACHINE COLLABORATION IN REMOTE WORK ENVIRONMENTS
type: item
description: This quantitative study investigates how data-driven mechanisms enhance human–machine collaboration in remote work environments by examining the relationships among automation transparency, workload balance, trust calibration, and collaboration efficiency. Using data from 912 participants across 87 distributed teams, the research applied multilevel regression and mediation analyses to quantify the cognitive, behavioral, and systemic factors that influence collaboration outcomes. Descriptive findings revealed that teams operating under transparent automation systems and balanced workload distributions exhibited significantly higher coordination and performance efficiency. Correlation and regression analyses demonstrated that automation transparency was the strongest predictor of collaboration efficiency (β = .38, p < .001), followed by workload balance (β = .29, p < .001) and automation intensity (β = .17, p < .001). Mediation testing confirmed that trust calibration partially mediated the relationship between transparency and efficiency, indicating that user confidence serves as a cognitive mechanism linking system clarity to improved performance. Reliability and validity assessments confirmed robust measurement consistency (α > .80; AVE > .50), and the overall model explained 61% of the variance in collaborative efficiency (R² = .61, p < .001). The findings highlight that human–machine synergy depends on measurable constructs of transparency, workload equity, and calibrated trust, all of which transform remote work into a data-driven ecosystem. The study contributes empirical evidence demonstrating that transparent algorithmic communication, equitable task distribution, and balanced automation intensity enhance coordination, decision accuracy, and trust stability within digital teams. It further establishes that data analytics provides a reliable framework for quantifying collaboration efficiency, thereby offering theoretical and practical insights into optimizing …
subject: Articles
  topic: section
subject: Human–Machine Collaboration
  topic: keywords
subject: Automation Transparency
  topic: keywords
subject: Trust Calibration
  topic: keywords
subject: Remote Work Systems
  topic: keywords
subject: Data-Driven Decision-Making
  topic: keywords
creator: Md Harun-Or-Rashid Mollah
creator: Sai Praveen Kudapa
rights: Copyright (c) 2022 Md Harun-Or-Rashid Mollah, Sai Praveen Kudapa (Author)
date: 2022-09-28
publication date: 2022-09-28
starting page: 47
ending page: 83
doi: 10.63125/wt9t6w68

MECHANISMS BY WHICH AI-ENABLED CRM SYSTEMS INFLUENCE CUSTOMER RETENTION AND OVERALL BUSINESS PERFORMANCE: A SYSTEMATIC LITERATURE REVIEW OF EMPIRICAL FINDINGS
type: item
description: This systematic literature review examined the mechanisms through which Artificial Intelligence–enabled Customer Relationship Management (AI-CRM) systems influence customer retention and overall business performance across diverse industrial contexts. A total of 72 empirical studies published between 2018 and 2022 were systematically analyzed to identify the quantitative pathways, mediating constructs, and moderating factors that explain AI’s impact on organizational and relational outcomes. The reviewed literature revealed that AI-CRM systems enhance performance through four primary mechanisms: data-driven personalization, cognitive automation, predictive and prescriptive analytics, and experience-based sentiment intelligence. These mechanisms collectively strengthen customer engagement, satisfaction, and loyalty while improving operational efficiency, decision-making accuracy, and profitability.The findings demonstrated that AI integration within CRM platforms produces significant positive effects on both financial and non-financial outcomes by transforming static data into actionable insights and adaptive customer interactions. Customer retention consistently emerged as the key mediating variable linking AI adoption intensity to profitability, confirming that technological sophistication translates into performance benefits primarily through long-term relational continuity. Moreover, firm size, digital readiness, and data governance maturity were identified as significant moderators that influenced the strength of AI’s impact, indicating that organizational context shapes the effectiveness of AI-CRM strategies.Across sectors such as banking, retail, telecommunications, and hospitality, evidence showed that predictive modeling and automation improved response times, reduced churn, and optimized marketing resource allocation. Personalization mechanisms were particularly influential in customer-facing industries, while cognitive automation drove process efficiency i…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: CRM Systems
  topic: keywords
subject: Customer Retention
  topic: keywords
subject: Business Performance
  topic: keywords
subject: Predictive Analytics
  topic: keywords
creator: Md Musfiqur Rahman
creator: Md.Kamrul Khan
rights: Copyright (c) 2023 Md Musfiqur Rahman, Md.Kamrul Khan (Author)
date: 2023-03-30
publication date: 2023-03-30
starting page: 31
ending page: 67
doi: 10.63125/qqe2bm11

THE ROLE OF DATA SCIENCE IN OPTIMIZING PROJECT EFFICIENCY AND INNOVATION IN U.S. ENTERPRISES
type: item
description: This study investigates The Role of Data Science in Optimizing Project Efficiency and Innovation in U.S. Enterprises, providing a comprehensive quantitative and literature-based assessment of how enterprise data science capability (EDSC) influences both operational performance and innovation outcomes. Drawing upon insights from 126 peer-reviewed journal articles and empirical studies published between 2010 and 2024, the research synthesizes theoretical perspectives from the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT) to examine how data-driven infrastructures, governance systems, and human capital integration contribute to measurable organizational advantages. Using multi-sectoral firm-level data encompassing manufacturing, healthcare, finance, and software industries, the study operationalizes EDSC through indicators such as data pipeline automation, analytics workforce density, governance quality, and predictive modeling adoption. Quantitative analysis demonstrates that enterprises with mature data science systems achieve, on average, 22% faster project cycle times, 9% greater cost stability, and 37% fewer quality defects compared to low-EDSC peers. Additionally, innovation outcomes—including patent productivity, citation intensity, and new-product revenue share—show strong elasticities with respect to EDSC, indicating that a 10% increase in data science capability corresponds to approximately 6–7% higher innovation output. The moderating influence of data governance quality and the mediating role of analytics human capital reveal that organizational structure and talent depth are crucial mechanisms that translate technical potential into realized value. Robustness checks, including instrumental-variable regressions and multi-group confirmatory factor analyses, confirm the validity and cross-sector stability of these findings. Collectively, this study provides empirical evidence that data science is not merely a technological tool but a strateg…
subject: Articles
  topic: section
subject: Data Science
  topic: keywords
subject: Project Efficiency
  topic: keywords
subject: Innovation
  topic: keywords
subject: Governance
  topic: keywords
subject: U.S. Enterprises
  topic: keywords
creator: Md Wahid Zaman Raj
rights: Copyright (c) 2025 Md Wahid Zaman Raj (Author)
date: 2025-10-10
publication date: 2025-10-10
starting page: 586
ending page: 600
doi: 10.63125/jzjkqm27

THE ROLE OF AI-DRIVEN CYBER RISK ANALYTICS ON CLOUD SECURITY POSTURE MANAGEMENT IN ENTERPRISE SYSTEMS
type: item
description: This study examines whether AI-driven cyber risk analytics improve Cloud Security Posture Management (CSPM) in enterprise systems and through which organizational mechanisms. We reviewed 47 prior studies to ground constructs and hypotheses, then executed a quantitative, cross-sectional, multi-case design across 220 cloud or security-team cases drawn from medium-to-large enterprises, with 512 survey responses synchronized to a ninety-day export of objective CSPM metrics. The problem addressed is persistent misconfiguration and alert overload in elastic, multi-tenant clouds that blunt security performance; the purpose is to quantify how analytics capability relates to measurable posture and to test the roles of triage efficiency and governed automation. Key variables include AI analytics capability, alert-triage efficiency, automation level, and CSPM outcomes such as misconfigurations per 100 resources, percent of critical findings remediated within policy windows, compliance score, mean time to detect, and mean time to remediate, with firm size, cloud tenure, provider mix, regulatory intensity, and account topology as controls. The analysis plan comprised reliability and validity checks, descriptive statistics, correlation matrices, hierarchical multiple regression with heteroskedasticity-robust inference, non-parametric bootstrapped mediation, and interaction-term moderation tests, plus robustness diagnostics. Headline findings show analytics capability is positively associated with stronger posture after controls, part of this relationship is mediated by improved alert triage, and the association is amplified at higher automation levels, indicating that explainable analytics plus governed automation yield the largest posture gains. Implications for practice are to invest in coverage-rich, explainable analytics, set explicit triage throughput objectives, and codify safe policy-as-code and auto-remediation so prioritized insights reliably become timely fixes; for sch…
subject: Articles
  topic: section
subject: AI-Driven Cyber Risk Analytics
  topic: keywords
subject: Cloud Security Posture Management
  topic: keywords
subject: Automation
  topic: keywords
subject: Alert Triage Efficiency
  topic: keywords
subject: Quantitative Cross-Sectional
  topic: keywords
creator: Anisur Rahman
rights: Copyright (c) 2025 Anisur Rahman (Author)
date: 2025-09-15
publication date: 2025-09-15
starting page: 649
ending page: 683
doi: 10.63125/fcgjv566

A SYSTEMATIC REVIEW OF DATA-DRIVEN BUSINESS PROCESS REENGINEERING AND ITS IMPACT ON ACCURACY AND EFFICIENCY CORPORATE FINANCIAL REPORTING
type: item
description: This systematic review investigates the transformative impact of data-driven business process reengineering (BPR) on the accuracy and efficiency of corporate financial reporting by synthesizing findings from 68 peer-reviewed empirical studies published between 2005 and 2024. The study examines how digital technologies, automation, analytics, and process optimization methodologies reshape financial reporting processes and outcomes across diverse organizational and regulatory contexts. Results from the meta-analysis reveal that data-driven BPR significantly enhances reporting performance, with pooled effect sizes indicating substantial improvements in reporting accuracy (Hedges’ g ≈ 0.58) and efficiency (Hedges’ g ≈ 0.64). Improvements in accuracy manifest in higher accruals quality, reduced restatement frequency, fewer audit adjustments, and stronger internal controls, while efficiency gains are observed through shorter close-to-disclose cycles, increased automation ratios, higher throughput, and reduced rework. The analysis highlights the differential contributions of various interventions, including enterprise resource planning (ERP) systems, robotic process automation (RPA), process mining, and continuous auditing, and demonstrates that the effectiveness of these interventions is strongly influenced by contextual factors such as data governance maturity, regulatory enforcement strength, technological integration depth, and industry-specific characteristics. Organizations operating in highly regulated environments and those with mature governance frameworks achieved the most pronounced performance gains, with financial services and technology sectors exhibiting the largest improvements. The review further identifies automation depth and real-time data utilization as critical drivers of both accuracy and efficiency, underscoring the strategic importance of integrating advanced technologies into financial reporting workflows. By consolidating evidence from a broad ra…
subject: Articles
  topic: section
subject: Business Process Reengineering
  topic: keywords
subject: Data-Driven
  topic: keywords
subject: Financial Reporting
  topic: keywords
subject: Accuracy
  topic: keywords
subject: Efficiency
  topic: keywords
creator: Md Arman Hossain
creator: Md.Kamrul Khan
rights: Copyright (c) 2022 Md Arman Hossain, Md.Kamrul Khan (Author)
date: 2022-12-28
publication date: 2022-12-28
starting page: 01
ending page: 41
doi: 10.63125/btx52a36

DATA DRIVEN BUSINESS INTELLIGENCE TOOLS IN AGRIBUSINESS A FRAMEWORK FOR EVIDENCE-BASED MARKETING DECISIONS
type: item
description: This study investigates the transformative role of data-driven business intelligence (BI) tools in enhancing evidence-based marketing decision-making within the agribusiness sector. Operating in an environment characterized by climatic variability, volatile demand, fragmented supply chains, and shifting consumer preferences, agribusiness increasingly depends on the ability to collect, integrate, and analyze large volumes of data to remain competitive. Business intelligence systems, encompassing data warehousing, predictive analytics, data mining, visualization dashboards, and machine learning algorithms, enable organizations to convert raw data into actionable insights that guide strategic and operational marketing decisions. This research is based on a comprehensive review of 156 peer-reviewed papers published in journals, conference proceedings, and industry reports, synthesizing theoretical frameworks, empirical findings, and practical applications of BI in agribusiness marketing. The study identifies key determinants of BI adoption, including data quality, system integration, analytics competence, governance structures, and leadership support, and examines how these factors influence BI use intensity and marketing outcomes. It also explores the integration of BI with digital agriculture technologies such as IoT devices and remote sensing systems, highlighting their combined potential to improve forecasting accuracy, enhance market segmentation, optimize pricing, and strengthen supply chain traceability. Quantitative evidence demonstrates that BI adoption significantly improves marketing decision quality, customer acquisition, market share, and overall performance, with decision quality acting as a key mediating factor. Additionally, digital-agriculture integration moderates the BI–performance relationship, amplifying its impact. The study concludes that data-driven BI tools provide a robust framework for transforming agribusiness marketing from intuition-based p…
subject: Articles
  topic: section
subject: Business Intelligence
  topic: keywords
subject: Agribusiness
  topic: keywords
subject: Evidence-Based Marketing
  topic: keywords
subject: Data Analytics
  topic: keywords
subject: Decision-Making
  topic: keywords
creator: SM. Toufiqur Rahman
creator: Abdul Hye
rights: Copyright (c) 2022 SM. Toufiqur Rahman, Abdul Hye (Author)
date: 2022-03-30
publication date: 2022-03-30
starting page: 35
ending page: 72
doi: 10.63125/p59krm34

INTEGRATION OF MACHINE LEARNING AND ADVANCED COMPUTING FOR OPTIMIZING RETAIL CUSTOMER ANALYTICS
type: item
description: This study examines how the joint deployment of machine learning (ML) and advanced computing (AC) optimizes retail customer analytics across forecasting, personalization, segmentation and targeting, churn/retention and customer lifetime value (CLV), pricing and promotions, and omni-channel operations. Drawing on a structured review of 150 peer-reviewed papers, we synthesize quantitative evidence on model performance (e.g., sequence models, tree ensembles, contextual bandits, survival models, and multimodal recommenders) alongside infrastructure choices (distributed clusters, accelerators, streaming feature stores, and CI/CD practices). We organize findings by prediction and decision tasks, data regimes (transactional, clickstream, catalog/price, and logistics), and compute regimes (batch vs. streaming; CPU vs. GPU/TPU), and we standardize outcomes using commonly reported accuracy, ranking, calibration, and utility measures together with operational key performance indicators such as service level, stock-out exposure, inventory turns, and on-time fulfillment. Across tasks, integrated ML+AC pipelines consistently outperform classical baselines when evaluation respects temporal order, features are governed for point-in-time correctness and freshness, and serving architectures meet tail-latency budgets. Retrieval→ranking recommenders, representation learning augmented with graph signals, and survival-aware churn models translate directly into measurable lift; hierarchical elasticity estimation and credible causal designs support interpretable pricing and promotion effects under operational constraints. Robustness and risk controls—drift surveillance, anomaly detection, adversarial-aware training, and privacy-preserving learning—stabilize performance under demand shocks and data shifts, while standardized cost and carbon accounting clarify the economic and environmental price of incremental accuracy. The synthesis shows that durable value in retail arises less from any s…
subject: Articles
  topic: section
subject: Retail
  topic: keywords
subject: Analytics
  topic: keywords
subject: Machine-learning
  topic: keywords
subject: Computing
  topic: keywords
subject: Personalization
  topic: keywords
creator: Hozyfa Shafa
rights: Copyright (c) 2022 Hozyfa Shafa (Author)
date: 2022-12-25
publication date: 2022-12-25
starting page: 01
ending page: 46
doi: 10.63125/p87sv224

AI-ENHANCED BUSINESS INTELLIGENCE DASHBOARDS FOR PREDICTIVE MARKET STRATEGY IN U.S. ENTERPRISES
type: item
description: This study examined whether AI-enhanced business intelligence (BI) dashboards measurably improve predictive market strategy in U.S. enterprises. Building on a systematic evidence scan of 138 peer-reviewed papers (2012–2025) and industry reports covering AI-assisted analytics, managerial decision support, and dashboard adoption, we designed and executed a multi-site quantitative evaluation combining a cluster randomized controlled trial with complementary observational analyses. The AI dashboard integrated model-generated demand forecasts, prescriptive nudges, and natural-language querying; the control condition used feature-parity descriptive BI without predictive or prescriptive components. Primary outcomes focused on forecast accuracy (MAPE and RMSE), with secondary endpoints including decision cycle time, campaign ROI, and monthly revenue growth. Mediation by user adoption (feature-use rate) and moderation by data maturity, firm size, and market volatility were pre-specified. Across participating business units, AI dashboards produced a statistically significant improvement in forecast accuracy (ATE −2.5 percentage points in MAPE), reduced decision cycle time (−18.2 hours), and increased campaign ROI (+13.6 pp) and revenue growth (+1.8 pp). Approximately one-third of the accuracy gain was mediated by adoption intensity, indicating behavioral uptake as a key pathway from capability to performance. Effects were larger in data-mature environments, while size and volatility showed limited moderating influence after multiplicity control. Rolling-origin back tests and Diebold–Mariano tests confirmed predictive uplift versus baseline models, and calibration diagnostics indicated reliable uncertainty communication. Sensitivity analyses (Did, quantile treatment effects, and per-protocol) supported robustness. Taken together, findings from both the empirical trial and the 138-paper evidence base suggest that AI-enhanced dashboards yield operationally meaningful gains with …
subject: Articles
  topic: section
subject: AI dashboards
  topic: keywords
subject: Predictive analytics
  topic: keywords
subject: Market strategy
  topic: keywords
subject: Business intelligence
  topic: keywords
subject: Randomized field experiment
  topic: keywords
creator: Md. Jobayer Ibne Saidur
rights: Copyright (c) 2025 Md. Jobayer Ibne Saidur (Author)
date: 2025-10-10
publication date: 2025-10-10
starting page: 603
ending page: 648
doi: 10.63125/8cvgn369

OPTIMIZING THE U.S. GREEN HYDROGEN ECONOMY: AN INTEGRATED ANALYSIS OF TECHNOLOGICAL PATHWAYS, POLICY FRAMEWORKS, AND SOCIO-ECONOMIC DIMENSIONS
type: item
description: The United States stands at a pivotal juncture in its energy transition, with green hydrogen emerging as a critical enabler for decarbonizing hard-to-abate sectors such as heavy industry, long-haul transportation, and long-duration energy storage. This journal review paper provides a comprehensive analysis of the U.S. perspective on the burgeoning green hydrogen economy, synthesizing current advancements, policy landscapes, and socio-economic implications. The review first examines the rapid technological progress underpinning the hydrogen value chain. It explores innovations in electrolysis, including declining costs for Proton Exchange Membrane (PEM) and Alkaline technologies, and the potential of emerging methods like solar-driven water splitting. Advancements in storage, from high-pressure tanks to geological solutions, and transport, via repurposed pipelines and liquid organic hydrogen carriers (LOHCs), are assessed. The paper also details the expanding applications of green hydrogen, from fertilizer production and refining to fuel cell electric vehicles and power generation. While prior studies have examined hydrogen technology or policy in isolation, this review uniquely integrates technological, regulatory, and socio-economic perspectives to outline a cohesive national roadmap for the U.S. hydrogen economy. It conducts a comparative review of national strategies, highlighting the transformative impact of the U.S. Inflation Reduction Act (IRA), particularly its hydrogen production tax credit (45V), which has positioned the nation as a highly competitive market. This is contrasted with the European Union’s targeted demand-side policies and the ambitious export-oriented strategies of nations in the Middle East and North Africa (MENA) region and the Global South, analyzing the implications for global trade and U.S. leadership. The paper concludes by identifying significant knowledge gaps and future research directions. These include the need for robust methodolo…
subject: Articles
  topic: section
subject: Hydrogen
  topic: keywords
subject: Hydrogen Economy
  topic: keywords
subject: US Hydrogen Pathway
  topic: keywords
subject: Green Hydrogen
  topic: keywords
creator: Sharif Md Yousuf Bhuiyan
creator: Md Shahadat Hossain
creator: Saleh Mohammad Mobin
creator: Mohammad Shahadat Hossain Sikdar
creator: Imtiaz Parvez
rights: Copyright (c) 2025 Sharif Md Yousuf Bhuiyan, Md Shahadat Hossain, Saleh Mohammad Mobin, Mohammad Shahadat Hossain Sikdar, Imtiaz Parvez (Author)
date: 2025-10-10
publication date: 2025-10-10
starting page: 586
ending page: 602
doi: 10.63125/xp8exe64

QUANTITATIVE USABILITY STUDY ON IOT-BASED HUMAN-CENTERED SECURITY INTERFACES FOR HOSPITAL CRITICAL INFRASTRUCTURE
type: item
description: The increasing digitization of hospital critical infrastructures has intensified the demand for secure yet highly usable interface systems that enable clinicians and administrative personnel to perform tasks efficiently without compromising data protection. This study presents a mixed-methods explanatory sequential investigation into the quantitative and behavioral dimensions of human-centered security interface design in healthcare environments. The research employed MATLAB-based statistical modeling and thematic analysis to evaluate how usability—defined through efficiency, satisfaction, and workload—mediates the relationship between system complexity and security compliance, while also examining workload as a moderating variable. Quantitative data were collected from 228 healthcare professionals across departments, and qualitative insights were drawn from 23 semi-structured interviews exploring perceptions of trust, cognitive fatigue, and workflow adaptation. The quantitative findings revealed that usability significantly predicts compliance (β = .61, p < .001) and mediates the adverse effects of system complexity on security adherence. Workload demonstrated a negative moderating influence, reducing the effectiveness of usability under cognitive strain. The structural equation model accounted for 59% of the variance in security performance, confirming the robustness of the proposed framework. Thematic analysis supported these results by identifying four key contextual determinants—cognitive overload, trust calibration, workflow disruption, and adaptive workarounds—that explain the behavioral underpinnings of compliance and noncompliance. Together, these findings substantiate that cybersecurity in healthcare is a socio-technical phenomenon in which human cognition and interface design jointly determine system resilience. The study contributes to the theoretical and practical advancement of human-centered cybersecurity by empirically validating the usability–securi…
subject: Articles
  topic: section
subject: Human-centered design
  topic: keywords
subject: Usability engineering
  topic: keywords
subject: Hospital critical infrastructure
  topic: keywords
subject: Security interface design
  topic: keywords
subject: Healthcare cybersecurity
  topic: keywords
creator: Siful Islam
rights: Copyright (c) 2025 Siful Islam (Author)
date: 2025-10-09
publication date: 2025-10-09
starting page: 559
ending page: 585
doi: 10.63125/4k914c72

THE IMPACT OF DATA-DRIVEN WEB FRAMEWORKS ON PERFORMANCE AND SCALABILITY OF U.S. ENTERPRISE APPLICATIONS
type: item
description: This systematic review examines how data-driven web frameworks shape performance and scalability outcomes in U.S. enterprise applications by integrating evidence across rendering strategies, data-access paradigms, runtime and placement topologies, and delivery protocols. Using a PRISMA-guided methodology and a registered protocol, we searched academic databases and high-rigor practitioner venues for studies published between 2014 and 2024, applied dual screening with adjudication, and retained 115 studies that reported transparent metrics under realistic workloads. Synthesized findings show consistent user-perceived gains when organizations adopt server-first rendering with streaming and disciplined hydration, especially when paired with cache-aware API contracts and explicit delivery priorities. For composite, authenticated views, GraphQL yields tail-latency and payload benefits when guarded by persisted queries, batching, and cost control, while REST remains superior for flat, cacheable reads that amplify edge hit ratios. Proximity emerges as a first-order lever: edge execution and multi-region placement reduce p95 and p99 only when data gravity follows compute, and targeted warm-path strategies are required for serverless estates to avoid cold-start penalties. Across the corpus, the most cost-effective improvements arise from cache-key normalization, deterministic revalidation, and precise prioritization that make early bytes visible to the network scheduler. We advocate a decision map that sequences investments from cache semantics and priorities to server-first plus streaming, to workload-appropriate API design, to selective edge and serverless adoption, all measured with percentile-aware telemetry and distributed tracing. This alignment consistently delivers low double-digit reductions in LCP and high-percentile latencies while protecting error budgets in enterprise conditions.
subject: Articles
  topic: section
subject: Data-Driven Web Frameworks
  topic: keywords
subject: Server-Side Rendering
  topic: keywords
subject: Graphql
  topic: keywords
subject: REST
  topic: keywords
subject: Caching And CDN
  topic: keywords
subject: HTTP/3
  topic: keywords
subject: Edge Computing
  topic: keywords
creator: Md Muzahidul Islam
rights: Copyright (c) 2025 Md Muzahidul Islam (Author)
date: 2025-10-09
publication date: 2025-10-09
starting page: 523
ending page: 558
doi: 10.63125/f07n4p12

SMART CORRIDOR SIMULATION FOR PEDESTRIAN SAFETY: : INSIGHTS FROM VISSIM-BASED URBAN TRAFFIC MODELS
type: item
description: Rapid urbanization and the growth of multimodal transport systems have increased the complexity of pedestrian–vehicle interactions at urban corridors, leading to heightened safety risks and congestion. This study develops a quantitative simulation framework using PTV VISSIM to evaluate how smart-corridor interventions—such as adaptive signal control, pedestrian detection systems, and geometric traffic-calming measures—can enhance pedestrian safety without compromising traffic efficiency. A 2.0-km urban corridor featuring multiple signalized and unsignalized crossings was modeled and calibrated with empirical traffic and pedestrian data collected from typical weekday and weekend conditions. Five simulation scenarios were constructed, including a baseline case and four smart-corridor configurations representing various technology integrations. Each scenario was replicated across different demand states using stochastic seeding to ensure statistical robustness. Conflict-based safety indicators such as Time-to-Collision (TTC), Post-Encroachment Time (PET), and Deceleration Rate to Avoid Crash (DRAC) were extracted from trajectory files using the Surrogate Safety Assessment Model (SSAM). These surrogate measures were normalized by pedestrian exposure to compute conflict rates per 1,000 crossings. Results were analyzed using negative binomial generalized linear mixed models (NB-GLMM) to estimate incidence rate ratios (IRRs) for both total and severe conflicts. Complementary linear mixed models (LMM) were applied to evaluate mobility outcomes including pedestrian delay, vehicle delay, and mean speed. Sensitivity analyses tested variations in threshold definitions (TTC, PET) and behavioral parameters to validate the robustness of findings. Preliminary results indicate that smart-corridor configurations integrating adaptive pedestrian detection and traffic-calming geometry achieved up to a 35% reduction in severe conflicts (IRR = 0.65, p < 0.05) while maintaining acceptable …
subject: Articles
  topic: section
subject: Smart Corridor
  topic: keywords
subject: Pedestrian Safety
  topic: keywords
subject: VISSIM Simulation
  topic: keywords
subject: Urban Traffic
  topic: keywords
subject: Surrogate Safety
  topic: keywords
creator: Mubashir Islam
rights: Copyright (c) 2021 Mubashir Islam (Author)
date: 2021-12-15
publication date: 2021-12-15
starting page: 33
ending page: 69
doi: 10.63125/b1bk0w03

IT AUTOMATION AND DIGITAL TRANSFORMATION STRATEGIES FOR STRENGTHENING CRITICAL INFRASTRUCTURE RESILIENCE DURING GLOBAL CRISES
type: item
description: This study addresses a pressing problem for operators of critical infrastructure: how to achieve dependable continuity and rapid recovery during global crises when complex, interdependent systems are under stress. The purpose is to quantify the associations between two strategic capabilities digital transformation strategy intensity and IT automation maturity and organizational resilience outcomes. Using a quantitative, cross-sectional, case-based design, we analyzed survey data from 156 cloud and enterprise cases spanning energy, healthcare, finance, telecommunications, transportation, and water. The work was grounded by a targeted review of 48 scholarly papers to inform construct definitions and instrumentation. Key variables included IT Automation Maturity, Digital Transformation Strategy Intensity, Crisis Severity, and controls for sector, size, legacy technology debt, and baseline cyber posture; the dependent variable was a composite Resilience Outcomes index covering service continuity, recovery speed, incident trends, and availability adherence. The analysis plan combined descriptive profiling, zero-order correlations, and hierarchical ordinary least squares with interaction and moderation terms, followed by robustness checks with sector fixed effects and telemetry-augmented outcomes. Headline findings show that both automation and transformation are positively associated with resilience, their interaction is synergistic, and the benefits of automation strengthen as crisis severity rises. Implications for practice are clear: pair architectural modernization cloud, governed data platforms, API-first and identity-centric controls with codified execution infrastructure-as-code, complete CI/CD pipelines, observability in delivery, progressive releases, and preapproved automated remediation to compress detection and restoration latencies and to localize failures across ecosystems.
subject: Articles
  topic: section
subject: Critical Infrastructure Resilience
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: IT Automation
  topic: keywords
subject: Infrastructure As Code
  topic: keywords
subject: Aiops
  topic: keywords
subject: Cloud Computing
  topic: keywords
creator: M.A. Rony
rights: Copyright (c) 2021 M.A. Rony (Author)
date: 2021-06-15
publication date: 2021-06-15
starting page: 01
ending page: 32
doi: 10.63125/8tzzab90

MODBUS/DNP3 OVER TCP/IP IMPLEMENTATION ON TMDSCNCD28388D AND ARDUINO WITH SIMULINK HMI FOR IOT-BASED CYBERSECURE ELECTRICAL SYSTEMS
type: item
description: The implementation of Modbus and DNP3 (Distributed Network Protocol) over TCP/IP represents a significant advancement in integrating industrial communication standards with modern IoT-based control and cybersecurity frameworks. This study presents a dual-platform experimental implementation of these protocols using the Texas Instruments TMDSCNCD28388D controlCARD and the Arduino Uno, each interfaced with a Simulink-based Human–Machine Interface (HMI). The system architecture enables seamless data exchange between field devices and supervisory applications over Ethernet, supporting real-time monitoring, remote actuation, and secure data acquisition. The TMDSCNCD28388D, equipped with a dual-core C2000 microcontroller and integrated F28388D processor, provides deterministic control for industrial nodes, while the Arduino Uno serves as a low-cost alternative for small-scale IoT testbeds. Both implementations employ Simulink models for system design, simulation, and code generation, ensuring modularity and platform independence. The study emphasizes the integration of industrial automation and IoT protocols within a cybersecurity-aware framework. A layered encryption model was incorporated into TCP/IP communication to evaluate data confidentiality, integrity, and resilience against common cyber threats such as spoofing and denial-of-service attacks. The Simulink HMI acts as both a visualization and command layer, enabling real-time supervisory control and anomaly detection through embedded MATLAB scripts and dashboard logic. Experimental results demonstrate high communication reliability, with Modbus achieving faster request–response cycles under low-load conditions, while DNP3 exhibited greater robustness against packet loss and network interference. The hybrid approach validates the feasibility of deploying standardized SCADA protocols in distributed IoT environments, supporting industrial cyber-physical systems where interoperability and security are critical. This wo…
subject: Articles
  topic: section
subject: Modbus/TCP
  topic: keywords
subject: DNP3 Protocol
  topic: keywords
subject: Simulink-Based HMI
  topic: keywords
subject: Industrial Internet of Things (IIoT)
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: SCADA Systems
  topic: keywords
creator: Waladur Rahman
creator: Jabed Hasan Tarek
rights: Copyright (c) 2025 Waladur Rahman, Jabed Hasan Tarek (Author)
date: 2025-10-07
publication date: 2025-10-07
starting page: 494
ending page: 522
doi: 10.63125/8e9cm978

SECURING ERP SYSTEMS: THE ROLE OF INFORMATION SECURITY ANALYSTS IN U.S. TEXTILE AND MANUFACTURING ENTERPRISES
type: item
description: This study addresses a persistent problem in U.S. textile and manufacturing enterprises: Enterprise Resource Planning environments are integral to operations yet remain vulnerable, and there is limited sector-specific, quantitative evidence on how information security analysts shape ERP security effectiveness. The purpose is to quantify the associations between analyst-centered capabilities and measurable ERP security outcomes. Design is quantitative, cross-sectional, and multi-case. The sample comprises N = 178 firm-level cases spanning cloud, on-premises, and hybrid ERP deployments. The literature review informing the framework synthesized 44 peer-reviewed sources. Key variables include an outcome composite for ERP security effectiveness incident frequency, severity, time to recover, segregation-of-duties posture, and audit signals, and focal predictors for analyst competency, staffing adequacy, and practice maturity, with security culture as a mediator and deployment model as a moderator. The analysis plan specified descriptive statistics, reliability checks, correlations, and multiple regression with heteroskedasticity-robust errors, plus mediation via bootstrap and moderation by cloud, with robustness checks using negative binomial for incident counts, ordinal logit for severity, and log-linear models for recovery time. Headline findings show analyst competency and practice maturity as the strongest unique predictors of effectiveness standardized β ≈ 0.28 and 0.31, adjusted R² ≈ 0.46, staffing adequacy is positive but smaller, security culture partially mediates these relationships, and the competency effect is steeper among cloud adopters; incident counts and recovery time improve materially incidence-rate ratios around 0.79 to 0.75 and 11 to 13 percent faster recovery per one-point increase on five-point scales. Implications are to prioritize ERP-specific IAM and SoD engineering, cadence-driven practice maturity, ERP-aware monitoring, and culture building, an…
subject: Articles
  topic: section
subject: ERP Security
  topic: keywords
subject: Information Security Analysts
  topic: keywords
subject: Textile Manufacturing
  topic: keywords
subject: Cloud ERP
  topic: keywords
subject: Segregation Of Duties
  topic: keywords
subject: Role-Based Access Control
  topic: keywords
subject: Practice Maturity
  topic: keywords
creator: Zamal Haider Shish
rights: Copyright (c) 2025 Zamal Haider Shish (Author)
date: 2025-10-07
publication date: 2025-10-07
starting page: 459
ending page: 493
doi: 10.63125/y8evt228

EFFECT OF TEMPERATURE‐DEPENDENT CRACK CLOSURE AND CRACK BRIDGING ON LONG CRACK PROPAGATION AT ELEVATED AND CYCLIC TEMPERATURE DURING HIGH CYCLE FATIGUE
type: item
description: This study addresses a persistent problem in high-cycle fatigue life prediction, observed growth-rate scatter under nominally similar ΔK and R conditions due to extrinsic shielding that varies with temperature. The purpose is to quantify how temperature-dependent crack closure and crack bridging relate to long-crack growth and how temperature level and within-cycle thermal range moderate these effects. Using a quantitative, cross-sectional, case-based design, we assembled a harmonized multi-case dataset and a structured literature corpus, reviewing 45 peer-reviewed papers to shape measurement rubrics and model specification. The empirical sample comprises enterprise-grade component cases from aerospace and power contexts with constant-elevated and cyclic temperature histories. Key variables include log(da/dN) as outcome, fracture-mechanics covariates ΔK, R, frequency, environment, surface finish, microstructure, temperature descriptors mean T, thermal mode, ΔT for cyclic cases, and mechanism indices for closure and bridging scored on five-point scales. The analysis plan applies hierarchical multiple regression with robust errors, entering controls, then mechanism indices, then prespecified moderation terms Temperature × Closure and Temperature × Bridging, with auxiliary ΔT terms for cyclic cases; robustness includes leave-one-case-out checks and an effective-range sensitivity using ΔKeff when opening fractions are available. Headline findings show ΔK and temperature are positively associated with growth, closure and bridging are protective with closure larger in magnitude, and temperature attenuates closure’s protection, ΔT raises growth and further erodes closure under cyclic service. Implications are practical, incorporate temperature mode and mechanism ratings into inspection planning and damage-tolerance models, report ΔK alongside mechanism indices or ΔKeff, and stratify assessments by thermal regime and environment.
subject: Articles
  topic: section
subject: Temperature-Dependent Crack Closure
  topic: keywords
subject: Crack Bridging
  topic: keywords
subject: High-Cycle Fatigue
  topic: keywords
subject: Thermomechanical Fatigue
  topic: keywords
subject: Paris Law
  topic: keywords
creator: Md Ariful Islam
rights: Copyright (c) 2025 Md Ariful Islam (Author)
date: 2025-09-15
publication date: 2025-09-15
starting page: 424
ending page: 458
doi: 10.63125/7vhtvh22

AI-AUGMENTED CYBERSECURITY: GRAPH NEURAL NETWORKS FOR PREDICTING NATION-STATE CYBERATTACKS
type: item
description: Nation-state cyberattacks represent one of the most complex and evolving threats to global security, often leveraging sophisticated strategies that exploit structural vulnerabilities across interconnected digital ecosystems. Traditional machine learning models, while effective for anomaly detection and malware classification, struggle to capture the relational and temporal dependencies inherent in coordinated cyber campaigns. This study explores the integration of Graph Neural Networks (GNNs) into AI-augmented cybersecurity frameworks to enhance predictive capabilities against nation-state cyberattacks. By modeling cyber infrastructures, threat intelligence, and attack pathways as graph-structured data, GNNs can identify latent patterns and interdependencies between threat actors, targets, and tactics. The proposed framework incorporates multi-source data, including network telemetry, open-source intelligence, and historical incident reports, to construct dynamic attack graphs that evolve in near real time. Experimental evaluations demonstrate that GNN-based models outperform conventional deep learning architectures in forecasting multi-stage intrusions, achieving higher precision in distinguishing state-sponsored campaigns from generic cyber threats. Furthermore, explainability modules embedded within the GNN pipeline improve interpretability by revealing critical nodes, links, and features driving predictions, thereby supporting actionable decision-making for security analysts and policymakers. This work underscores the strategic potential of AI-augmented approaches in advancing national resilience, providing early-warning capabilities, and enabling proactive defense strategies against adversarial state actors.
subject: Articles
  topic: section
subject: Graph Neural Networks (GNNs)
  topic: keywords
subject: Nation-State Cyberattacks
  topic: keywords
subject: AI-Augmented Cybersecurity
  topic: keywords
subject: Threat Intelligence Graphs
  topic: keywords
subject: Predictive Defense Systems
  topic: keywords
creator: Sai Srinivas Matta
creator: Manish Bolli
rights: Copyright (c) 2024 Sai Srinivas Matta, Manish Bolli (Author)
date: 2024-12-25
publication date: 2024-12-25
starting page: 35
ending page: 59
doi: 10.63125/z3dy9737

AI-DRIVEN BUSINESS ANALYTICS FOR COMPETITIVE ADVANTAGE IN SERVICE-ORIENTED ENTERPRISES: CUSTOMER EXPERIENCE AND EFFICIENCY
type: item
description: Drawing on a PRISMA-guided systematic review, this paper synthesizes evidence on how AI-driven business analytics create competitive advantage in service-oriented enterprises by improving customer experience and operational efficiency. Searches across major databases (Scopus, Web of Science, IEEE Xplore, ACM, ScienceDirect, ABI/INFORM, Emerald, PubMed), complemented by snowballing, yielded 115 peer-reviewed studies from 2015 to 2025. Across the corpus, four in five studies reported improvement in at least one focal outcome, 45.2 percent achieved joint gains in experience and efficiency, 10.4 percent showed trade-offs, and only 3.5 percent reported deterioration. Mechanisms associated with joint gains include conversational AI and agent assist, prescriptive routing and scheduling with reinforcement learning or optimization, and forecast-to-schedule loops; voice-of-customer text and speech analytics and personalization consistently lift experience, while process mining, robotic process automation, and capacity planning reduce cycle time, queues, and cost-to-serve. Deployment pattern and governance matter: human in the loop configurations and programs with privacy safeguards, drift monitoring, fairness checking, and override policies halve the incidence of trade-offs compared with low maturity implementations. Sector analyses indicate that data rich, SLA intense contexts such as telecom, financial services, and logistics often realize balanced benefits, while public services and healthcare see efficiency first unless communication and escalation are redesigned in tandem. Methodologically, stronger designs confirm that effects persist when prediction is coupled to decision rights and evaluated with value linked metrics. The review offers an integrative framework and actionable guidance for prioritizing mechanisms that change decisions in the flow of work and for institutionalizing scalable analytics capabilities.
subject: Articles
  topic: section
subject: AI-Driven Business Analytics
  topic: keywords
subject: Customer Experience
  topic: keywords
subject: Operational Efficiency
  topic: keywords
subject: Service-Oriented Enterprises
  topic: keywords
subject: Process Mining
  topic: keywords
subject: Conversational AI
  topic: keywords
subject: Personalization
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
creator: Sharmin Akter
creator: S M Rukaya Akhter
creator: Mohsina Binte Rahman
creator: Prashanta Naidu
rights: Copyright (c) 2025 Sharmin Akter, S M Rukaya Akhter, Mohsina Binte Rahman, Prashanta Naidu (Author)
date: 2025-09-15
publication date: 2025-09-15
starting page: 389
ending page: 423
doi: 10.63125/mx0k6019

THE IMPACT OF DATA-DRIVEN INDUSTRIAL ENGINEERING MODELS ON EFFICIENCY AND RISK REDUCTION IN U.S. APPAREL SUPPLY CHAINS
type: item
description: This study investigated whether data-driven industrial engineering models improved efficiency and reduced risk in U.S. apparel supply chains, a domain with high clockspeed and volatile demand where causal mechanisms were often under-specified. The purpose was to estimate associations between the adoption intensity of analytics-supported line balancing, statistical process control, stochastic inventory and scheduling, and simulation-backed capacity planning and two outcome families: operational efficiency and operational or supply risk. The design was quantitative, cross-sectional, and multiple-case. A focused narrative review of 44 peer-reviewed studies informed construct definitions and item wording. The sample comprised 208 analyzable cloud and enterprise cases drawn from brand owners, contract manufacturers, and distribution partners, with managers as respondents and optional KPI uploads. Key variables included adoption, process standardization as a mediator, supply chain complexity as a moderator, and composites for efficiency (on-time in-full, order cycle time, throughput, unit cost, rework, appropriately reverse coded) and risk (disruption frequency, lead-time variability, stockout, returns or defects, compliance). The analysis plan specified descriptives, correlations, ordinary least squares with robust or clustered standard errors and a common control set (size, automation, capital intensity, market segment, nearshoring ratio, supplier reliability, demand clockspeed), moderated models with mean-centered interactions, bootstrapped indirect effects for mediation, and robustness checks including alternative index constructions and influence diagnostics. Headline findings indicated that higher adoption was associated with higher efficiency and lower risk after controls, with partial mediation through process standardization and attenuation of effects at very high complexity; results were stable across robustness specifications. Implications for practice included…
subject: Articles
  topic: section
subject: Data-Driven Industrial Engineering
  topic: keywords
subject: Apparel Supply Chains
  topic: keywords
subject: Operational Efficiency
  topic: keywords
subject: Risk Reduction
  topic: keywords
subject: Statistical Process Control
  topic: keywords
subject: Line Balancing
  topic: keywords
creator: Md. Tahmid Farabe Shehun
rights: Copyright (c) 2025 Md. Tahmid Farabe Shehun (Author)
date: 2025-09-30
publication date: 2025-09-30
starting page: 353
ending page: 388
doi: 10.63125/y548hz02

A SYSTEMATIC REVIEW ON THE IMPACT OF NFPA-COMPLIANT FIRE PROTECTION SYSTEMS ON U.S. INFRASTRUCTURE RESILIENCE
type: item
description: This systematic review evaluates how NFPA-compliant fire protection systems contribute to infrastructure resilience in the United States by linking code-conforming design and maintenance to measurable outcomes. Following PRISMA 2020, we searched major databases and grey sources, screened records in duplicate, extracted standardized measures, appraised risk of bias, and synthesized evidence using random effects and narrative methods. In total, 115 studies met the inclusion criteria, spanning healthcare, industrial and energy facilities, transportation tunnels and stations, data centers, and high-rise residential and commercial buildings. Across the corpus, verified compliance and disciplined inspection, testing, and maintenance were consistently associated with better life-safety and continuity outcomes, including higher odds of confinement to room or area of origin, lower injuries and direct losses, reduced nuisance alarms, and shorter downtime. Paired systems performed best; detection with automatic suppression yielded additive gains, and smoke management improved egress tenability under door-open conditions. Moderators included governance quality, building vintage, hazard severity, and water or power redundancy. Framed through robustness, redundancy, resourcefulness, and rapidity, findings show that NFPA-aligned hardware governed by auditable compliance and maintenance functions as an engineered risk control that compresses severe-event tails and accelerates recovery. Economic signals from studies reporting business interruption aligned with physical loss containment, supporting favorable present-value cases for sustained compliance and maintenance programs. Practically, the review recommends outcome-oriented acceptance testing tuned to credible scenarios, portfolio dashboards tracking confinement and restoration metrics, and prioritization of high-leverage components such as pumps, supervisory valves, detection networks, and smoke control interfaces to deliver re…
subject: Articles
  topic: section
subject: NFPA Compliance
  topic: keywords
subject: Fire Protection Systems
  topic: keywords
subject: Infrastructure Resilience
  topic: keywords
subject: Automatic Sprinklers
  topic: keywords
subject: Fire Detection and Alarm
  topic: keywords
subject: Smoke Control
  topic: keywords
creator: Md. Milon Mia
rights: Copyright (c) 2025 Md. Milon Mia (Author)
date: 2025-09-15
publication date: 2025-09-15
starting page: 324
ending page: 352
doi: 10.63125/ne3ey612

DIGITAL ENGINEERING AND PROJECT MANAGEMENT FRAMEWORKS FOR IMPROVING SAFETY AND EFFICIENCY IN US CIVIL AND RAIL INFRASTRUCTURE
type: item
description: This study addresses a persistent problem in US civil and rail delivery: preventable safety incidents and schedule and cost drift rooted in fragmented information and inconsistent governance. Purpose was to quantify how digital engineering adoption and project management framework maturity relate to safety and efficiency. Design was quantitative, cross sectional, and case based, combining a survey with audited archival records. Sample comprised 168 US civil and rail projects from owners and prime contractors. Key variables were two Likert composites for digital engineering adoption and framework maturity, outcomes of total recordable and lost time incident rates plus schedule and cost performance indices, a rail versus non rail sector moderator, and controls for value, duration, delivery method, organization size, complexity, and region. Analysis plan included reliability checks, correlation matrices, ordinary least squares for efficiency with robust errors, and Poisson or Negative Binomial models for safety counts with log exposure offsets, followed by moderation tests and robustness checks. Headline findings show that higher adoption and higher maturity were independently associated with lower incident rates and schedule and cost performance after adjustment, with a stronger adoption to schedule link in rail settings; results were stable across alternative codings and inferences. The literature review informing constructs and hypotheses synthesized 44 peer reviewed sources. Implications for agencies include mandating ISO aligned information requirements and common data environment compliance, requiring 4D sequencing for possession constrained works, strengthening stage gates, risk and change control, and institutionalizing benefits management and lessons learned to sustain gains and transparency across programs.
subject: Articles
  topic: section
subject: Digital Engineering
  topic: keywords
subject: Building Information Modeling
  topic: keywords
subject: 4D Planning
  topic: keywords
subject: Common Data Environment
  topic: keywords
subject: Project Governance
  topic: keywords
subject: Safety Performance
  topic: keywords
creator: Syed Zaki Uddin
rights: Copyright (c) 2025 Syed Zaki Uddin (Author)
date: 2025-09-29
publication date: 2025-09-29
starting page: 294
ending page: 323
doi: 10.63125/0w76pn26

INNOVATION OF BIODEGRADABLE ANTIMICROBIAL FABRICS FOR SUSTAINABLE FACE MASKS PRODUCTION TO REDUCE RESPIRATORY DISEASE TRANSMISSION
type: item
description: This study addresses a dual problem: widespread dependence on persistent polypropylene mask media that exacerbates waste streams, and uneven real-world protection due to variability in community mask materials and hygiene. The purpose is to evaluate whether biodegradable antimicrobial fabrics can satisfy practical respiratory performance and sustainability requirements without eroding user adoption. Using a quantitative, cross-sectional, multi-case design, we integrated case-level laboratory anchors with respondent perceptions across three industry-relevant enterprise cases cellulose-rich woven or knit, PLA-based nonwoven, and cotton or biopolymer blends sampled in healthcare, retail, education, and community settings (N ≈ 560). A structured literature review of 47 peer-reviewed studies informed construct definitions and technical thresholds. Key variables included filtration efficiency across submicron aerosols, pressure drop as a breathability proxy, antimicrobial log-reduction on fabric surfaces, and five Likert-scale drivers perceived antimicrobial efficacy, filtration confidence, comfort or breathability, sustainability perception, and cost concern with adoption intention and reported weekly use as outcomes and perceived risk as a moderator. The analysis plan specified descriptive summaries, correlations, ordinary least squares with case fixed effects for intention, ordered logit for use, an efficacy by risk interaction, and prespecified robustness checks. Headline findings show perceived antimicrobial efficacy and filtration confidence as the largest unique associates of intention, comfort contributes positively, sustainability adds a modest incremental lift, and cost concern suppresses intention; intention predicts weekly use, and higher perceived risk strengthens the efficacy to intention pathway, with an exploratory signal linking more frequent use to fewer recent respiratory symptoms. Implications emphasize specifying breathable submicron filtration, durab…
subject: Articles
  topic: section
subject: Biodegradable Antimicrobial Fabrics
  topic: keywords
subject: Face Masks
  topic: keywords
subject: Filtration Efficiency
  topic: keywords
subject: Pressure Drop
  topic: keywords
subject: Sustainability
  topic: keywords
subject: Adoption Intention
  topic: keywords
subject: Perceived Risk
  topic: keywords
creator: Md Rezaul Karim
rights: Copyright (c) 2021 Md Rezaul Karim (Author)
date: 2021-12-15
publication date: 2021-12-15
starting page: 01
ending page: 31
doi: 10.63125/ba6xzq34

AI-DRIVEN WATER PURIFICATION MODEL IMPLEMENTATION IN SMART CITIES: REAL-TIME SOLAR DESALINATION AND EFFLUENT TREATMENT
type: item
description: The rapid urbanization and population growth in smart cities have intensified the demand for sustainable, efficient, and resilient water purification solutions. Traditional methods of desalination and effluent treatment often face challenges such as high energy consumption, operational inefficiency, and limited adaptability to fluctuating urban water needs. This paper proposes an integrated AI-driven water purification model that combines real-time solar-powered desalination systems with advanced effluent treatment mechanisms, optimized through predictive machine learning algorithms. The model leverages artificial intelligence to monitor, forecast, and regulate critical parameters, including salinity, turbidity, energy input, and contaminant levels, ensuring dynamic resource allocation and system stability. A hybrid framework is developed wherein solar-powered desalination provides a sustainable clean water source, while AI-enhanced effluent treatment units recycle wastewater streams, reducing environmental burden and promoting circular water use. The proposed system is tested through simulation and pilot-level validation, demonstrating significant improvements in purification efficiency, reduction in energy intensity, and adaptive responsiveness to varying urban water demands. Results indicate that AI optimization enables a reduction in operational costs by enhancing predictive maintenance, minimizing downtime, and improving energy utilization of photovoltaic modules. Furthermore, the integration of real-time analytics facilitates smart decision-making, aligning with the sustainability objectives of smart cities by reducing greenhouse gas emissions and ensuring water security. The findings of this study suggest that AI-driven solar desalination and effluent treatment not only address critical challenges of urban water management but also serve as a scalable and replicable model for global smart city applications. This research contributes to the evolving discourse …
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Solar Desalination
  topic: keywords
subject: Effluent Treatment
  topic: keywords
subject: Smart Cities
  topic: keywords
subject: Water Purification
  topic: keywords
creator: Md Rashedul Islam
rights: Copyright (c) 2025 Md Rashedul Islam (Author)
date: 2025-09-27
publication date: 2025-09-27
starting page: 270
ending page: 293
doi: 10.63125/fbxtdt92