American Journal of Advanced Technology and Engineering Solutions
The American Journal of Advanced Technology and Engineering Solutions (AJATES) Online ISSN (3067-0470) is a leading international, peer-reviewed, open-access journal dedicated to publishing high-quality research and advancements in technology and engineering. AJATES serves as a platform for researchers, academicians, industry professionals, and innovators to present cutting-edge solutions, novel methodologies, and interdisciplinary studies that address contemporary challenges in engineering and technology. Published monthly, AJATES focuses on fostering innovation and collaboration across diverse disciplines, including mechanical engineering, electrical engineering, computer science, materials science, robotics, and sustainable technologies. By bridging the gap between theory and practice, the journal contributes to the global discourse on technological advancements and their practical applications.
DATA-DRIVEN SUPPLY CHAIN RESILIENCE MODELING THROUGH STOCHASTIC SIMULATION AND SUSTAINABLE RESOURCE ALLOCATION ANALYTICS
This study investigates how data-driven capabilities and sustainable resource-allocation policies jointly influence supply chain resilience (SCRes) under disruption and uncertainty. Adopting a quantitative, cross-sectional, multiple case-study design, the research integrates survey-based measurement, archival operational data, and stochastic simulation–optimization to link organizational capabilities—visibility, collaboration, flexibility, supplier diversification, redundancy, risk orientation, and allocation efficiency—to key resilience outcomes, including service recovery, time-to-recovery (TTR), backorder intensity, cost variance, and emissions. Data were collected from 190 professionals across four international firms in discrete manufacturing, FMCG, healthcare logistics, and electronics sectors, each providing both perceptual and objective data spanning 12–24 months of operations. Hierarchical multiple regression models, supported by mediation and moderation analyses, revealed that collaboration, digital visibility, and allocation efficiency were the strongest predictors of resilience performance, while flexibility and diversification contributed moderate incremental effects. Allocation efficiency partially mediated the impact of visibility and collaboration on outcomes, demonstrating that information and coordination enhance resilience primarily through improved resource allocation. Moreover, capability effects intensified under uncertainty—visibility’s and collaboration’s benefits were significantly amplified by demand volatility and lead-time variability. A composite Resilience Performance Index (RPI) was developed to align statistical and operational metrics, linking survey constructs to observed KPIs. Monte Carlo simulation experiments using empirically calibrated disruption parameters validated these statistical insights: sustainability-aware optimized policies improved mean service levels by 3–6 percentage points, reduced TTR by 15–27%, lowered backorder intensity by up to 24%, and cut emissions intensity by 6–11% compared to status quo. These findings confirm that resilience and sustainability can be jointly enhanced through data-driven allocation strategies. The study contributes an integrated, replicable framework that bridges measurement, inference, and decision experimentation—offering both theoretical clarity on capability mechanisms and practical guidance for managers seeking to design resilient, carbon-conscious supply chains under stochastic conditions.
Copyright (c) 2024 Sudipto Roy, Md. Hasan Imam (Author)
INTEGRATING TABLEAU, SQL, AND VISUALIZATION FOR DASHBOARD-DRIVEN DECISION SUPPORT: A SYSTEMATIC REVIEW
This systematic review investigates the performance, optimization, and governance practices of business intelligence (BI) systems with a focus on SQL-driven dashboards, streaming pipelines, cloud scalability, and organizational integration. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 142 studies published between 2005 and 2024 were systematically identified, screened, and synthesized. The review highlights several key findings. First, SQL continues to serve as the foundational language in BI, maintaining its dominance through adaptability across distributed and hybrid environments. Second, dashboards have evolved beyond their original descriptive function, now acting as governance instruments that embed compliance, accountability, and transparency into organizational workflows. Third, sectoral and regional variations demonstrate that BI adoption and optimization are shaped by contextual factors, with financial and healthcare sectors prioritizing compliance and risk management, while retail and e-commerce emphasize customer analytics and speed. Fourth, performance optimization remains a critical concern, with techniques such as query pushdown, incremental refresh, caching, and streaming pipelines shown to reduce latency and improve scalability in real-time analytics. Fifth, monitoring, testing, and maintainability practices are increasingly emphasized, aligning BI with DevOps principles and continuous integration frameworks. Additionally, cloud and hybrid scalability emerge as both technical and economic challenges, requiring elastic infrastructures balanced against cost and governance demands. Finally, the review underscores the need for conceptual integration of technical, organizational, and governance dimensions, as BI success depends on harmonizing these three axes. Collectively, the findings confirm earlier scholarship while extending it with new empirical and conceptual insights, illustrating that BI has matured into a strategic infrastructure supporting decision-making, regulatory compliance, and organizational resilience. This review provides actionable recommendations for practitioners and highlights research gaps that call for deeper exploration of cross-sectoral comparisons, governance integration models, and economic trade-offs in BI scalability.
Copyright (c) 2023 Momena Akter, Md Nur Hasan Mamun (Author)
ROLE OF DIGITAL TWINS AND BIM IN U.S. HIGHWAY INFRASTRUCTURE ENHANCING ECONOMIC EFFICIENCY AND SAFETY OUTCOMES THROUGH INTELLIGENT ASSET MANAGEMENT
To synthesize evidence on how Building Information Modeling (BIM) and digital twins enhance economic efficiency and safety in U.S. highway infrastructure through intelligent asset management. Guided by PRISMA 2020, searches across engineering, transportation, and standards repositories (2000–August 2023) yielded 3,146 records. After removing 1,012 duplicates, 2,134 titles/abstracts were screened; 378 full texts were assessed; 92 studies were included (empirical, mixed-methods, and rigorously reported gray literature). Data were extracted on model uses, governance, and outcomes; heterogeneity precluded meta-analysis, so a structured narrative synthesis was undertaken. Evidence clustered across four domains. (1) Economic efficiency: sixty-three studies reported fewer design clashes, reduced rework and RFIs, and more stable schedules and quantities under 3D/4D/5D coordination. (2) Predictive maintenance and lifecycle value: forty-nine studies linked digital-twin condition monitoring and deterioration forecasting to earlier, better-targeted treatments and fewer emergency interventions. (3) Safety and risk reduction: fifty-two studies associated time-aware staging, traffic management visualizations, and real-time hazard indices with lower exposure in work zones and shorter detection-to-response intervals. (4) Intelligent asset management: fifty-eight studies documented benefits when digital as-builts, IFC/COBie handovers, and sensor feeds populated governed asset registers, improving auditability and budget formulation. Cross-jurisdictional comparisons (forty-one studies) showed stronger, more consistent outcomes where open standards and owner information requirements were embedded in procurement and delivery. Across ninety-two studies, BIM provides the authoritative, object-based backbone for asset information, while digital twins synchronize that information with operational state. Under interoperable standards and clear governance, this pairing consistently supports cost control, safety performance, and reliable maintenance planning in U.S. highway programs.
Copyright (c) 2023 Mubashir Islam, MKA Shahinoor Rahman Jahid (Author)
DATA-DRIVEN DECISION-MAKING THROUGH CUSTOMER RELATIONSHIP MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW IN MODERN ENTERPRISES
This study presents a systematic review of the integration between Customer Relationship Management (CRM) and Data-Driven Decision-Making (DDDM) in modern enterprises, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure rigor and transparency. A total of 1,264 records were initially identified, of which 156 studies met the inclusion criteria after duplicate removal, screening, and full-text assessment. The reviewed literature spans conceptual models, technological enablers, sector-specific applications, international perspectives, and organizational challenges. Findings indicate that CRM is conceptualized not merely as a technological system but also as a strategic orientation grounded in relationship marketing, while DDDM is positioned as both an analytical process and a managerial philosophy. Together, these frameworks enable enterprises to enhance customer insights, optimize decision-making, and strengthen long-term competitiveness. Technological enablers such as cloud computing, Software-as-a-Service platforms, artificial intelligence, machine learning, and social media analytics were identified as pivotal in transforming CRM systems into predictive and real-time decision-making tools. Sector-specific applications demonstrated consistent value in retail, e-commerce, banking, financial services, and healthcare, highlighting the adaptability of CRM-DDDM integration across industries. International perspectives revealed that while CRM-DDDM principles are globally relevant, adoption and effectiveness vary according to cultural, institutional, and digital maturity factors, underscoring the importance of contextual adaptation. Challenges were also noted, particularly organizational resistance, poor data quality, system complexity, ethical concerns, and difficulties in measuring return on investment, all of which continue to limit effectiveness. The review further identified critical debates regarding technology versus relationship orientation, the appropriateness of customer equity versus firm performance as success metrics, and the absence of unified theoretical frameworks. Collectively, this synthesis demonstrates that CRM-DDDM integration delivers significant strategic and operational benefits while remaining shaped by contextual and organizational constraints. The study contributes by consolidating evidence across disciplines and offering recommendations for both research and practice.
Copyright (c) 2024 Md. Musfiqur Rahman (Author)
COMPARATIVE ANALYSIS OF PETROLEUM INFRASTRUCTURE PROJECTS IN SOUTH ASIA AND THE US USING ADVANCED GAS TURBINE ENGINE TECHNOLOGIES FOR CROSS INTEGRATION
This study presents a comparative analysis of petroleum infrastructure projects in South Asia and the United States, with a focus on the role of advanced gas turbine engine technologies in enabling effective cross integration between fuel supply systems and power generation assets. The research followed a systematic methodology to ensure transparency and rigor, drawing upon a comprehensive body of 92 peer-reviewed articles and technical reports, collectively cited over 1,850 times, to evaluate infrastructure maturity, technological deployment, governance structures, operational resilience, and environmental performance across both regions. The findings highlight stark contrasts: the United States demonstrates a highly developed and interconnected petroleum infrastructure anchored by extensive pipelines, underground storage, LNG export facilities, and a diverse mix of heavy-duty and aeroderivative turbines optimized for flexible grid participation. South Asia, in contrast, exhibits a more fragmented infrastructure profile characterized by concentrated LNG terminals, limited cross-border pipelines, and heavy reliance on baseload combined-cycle units that often face challenges linked to fuel quality variability, maintenance dependence, and climatic vulnerabilities. Environmental and safety standards also diverge significantly, with the U.S. operating under stringent regulatory regimes and continuous monitoring systems, while South Asia faces weaker enforcement capacity and greater exposure to natural hazards. Governance and finance emerge as pivotal factors, as U.S. projects benefit from mature markets and diversified capital flows, whereas South Asian projects remain heavily reliant on donor financing and sovereign guarantees. The study concludes that cross integration is not solely a technical challenge but one shaped by infrastructure maturity, institutional strength, and environmental resilience. By synthesizing insights from a broad literature base, this research provides an integrated framework for understanding the systemic differences between South Asia and the United States and underscores the need for region-specific strategies to optimize petroleum-to-turbine integration.
Copyright (c) 2022 Sabuj Kumar Shil, Zobayer Eusufzai (Author)
PROSPECT OF USING AI- INTEGRATED SMART MEDICAL TEXTILES FOR REAL-TIME VITAL SIGNS MONITORING IN HOSPITAL MANAGEMENT & HEALTHCARE INDUSTRY
This study explores the prospect of using AI-integrated smart medical textiles for real-time vital signs monitoring in hospital management and the broader healthcare industry. Smart textiles, which embed biosensors and conductive materials within wearable fabrics, have evolved from experimental prototypes into clinically relevant tools capable of continuously tracking physiological parameters such as cardiac activity, respiratory function, body temperature, oxygen saturation, blood flow, and muscle activity. The integration of artificial intelligence enables advanced signal processing, artifact suppression, predictive modeling, and real-time classification of health events, thereby transforming raw data into actionable clinical insights. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 152 peer-reviewed articles were systematically reviewed, collectively cited more than fifteen thousand times, ensuring a comprehensive synthesis of technological, clinical, operational, and regulatory perspectives. The findings revealed that AI-enhanced smart textiles deliver accuracy comparable to conventional monitoring devices while offering advantages in comfort, mobility, and continuous data capture. Hospitals implementing these systems reported operational benefits such as reduced nurse workload, improved alarm management, and expanded monitoring coverage, alongside clinical gains including earlier detection of patient deterioration, reductions in adverse events, shortened lengths of stay, and decreased readmissions. International case studies highlighted adoption across Europe, North America, and Asia-Pacific, while regulatory discussions underscored the importance of compliance with FDA, EMA, ISO, and IEC standards for successful deployment. Beyond inpatient care, the review identified applications in rehabilitation, chronic disease management, maternal and neonatal monitoring, occupational health, and telehealth integration, illustrating the adaptability of these technologies across healthcare contexts. Overall, the synthesis demonstrates that AI-integrated smart medical textiles represent a transformative innovation, offering both clinical and operational value, with strong potential to reshape hospital management and support the global transition toward more patient-centered, data-driven healthcare systems.
Copyright (c) 2024 Md Rezaul Karim, Md Takbir Hossen Sarker (Author)
EVALUATING AGILE BUSINESS ANALYSIS IN POST-COVID RECOVERY A COMPARATIVE STUDY ON FINANCIAL RESILIENCE
This study examines the role of agile business analysis in strengthening financial resilience during the post-COVID recovery period, with a focus on organizational adaptability across different industries, firm sizes, and regional contexts. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, a total of 86 scholarly articles were systematically reviewed, covering both empirical and conceptual contributions. The analysis reveals that agile business analysis enhances resilience through mechanisms such as liquidity preservation, cost efficiency, improved revenue durability, and the integration of financial guardrails into iterative decision-making processes. The findings demonstrate that while small and medium-sized enterprises primarily rely on agile practices to ensure short-term survival and cash preservation, large multinationals employ scaled frameworks to balance global strategies with localized adaptation. Sectoral variations highlight the flexibility of agile analysis, as healthcare, retail, technology, and manufacturing organizations adapted its practices to their specific challenges. Regional comparisons further show that institutional, cultural, and regulatory factors shape the way agile analysis contributes to financial resilience, with significant differences observed between North America, Europe, and Asia-Pacific. The study also underscores the importance of embedding governance, compliance, and risk management within agile practices to balance adaptability with accountability. Collectively, the evidence positions agile business analysis as a strategic capability that extends beyond project delivery to function as a financial resilience framework. By synthesizing insights from 86 reviewed studies, this research contributes to the understanding of how agile practices enable organizations to recover effectively, safeguard financial stability, and sustain performance in environments characterized by volatility and uncertainty.
Agile Business Analysis Strengthens Resilience
Financial Adaptability Supports Organizational Survival
Post-COVID Recovery Reshapes Business Practices
Copyright (c) 2022 Md Hasan Zamil, Moin Uddin Mojumder (Author)
IMPACT OF PREDICTIVE DATA MODELING ON BUSINESS DECISION-MAKING: A REVIEW OF STUDIES ACROSS RETAIL, FINANCE, AND LOGISTICS
This study investigates how predictive data modeling influences business decision-making across retail, finance, and logistics, emphasizing the practices that convert predictive accuracy into measurable organizational impact. Evidence from 100 peer-reviewed empirical studies linking predictive outputs to operational actions such as inventory replenishment, dynamic pricing, credit approvals, fraud triage, routing optimization, and service-level promise windows was synthesized. Studies employing temporal validation, probability calibration, explicit operating thresholds, and structured translation into operational policies reported business improvements in 93 percent of cases, achieving median gains of approximately 9–12 percent on the primary KPI. In contrast, minimally aligned designs succeeded in only 48 percent of cases, with modest gains of about 3–5 percent. Sector-specific results revealed consistent yet domain-sensitive patterns. In retail, hierarchical forecasting methods and decision-aware pricing systems yielded a median 2.8 percentage-point reduction in stockouts and a 2.2 percent revenue lift when forecast distributions were directly integrated into service curves and inventory or pricing rules. In finance, calibrated scorecards, cost-sensitive thresholds, and temporally validated probability estimates reduced expected credit loss by nearly 8 percent at constant approval rates or raised approvals by approximately 3.5 percentage points at constant risk levels. Fraud detection and anti–money laundering systems achieved a median 22 percent reduction in false positives while improving cost per true positive when precision–recall evaluation, network features, and workload-aware thresholds informed operational decision-making. In logistics, uncertainty-aware demand and travel-time prediction models enhanced on-time delivery by about 3.9 percentage points and reduced routing costs by nearly 6 percent when embedded into promise windows, lateness-penalty formulations, and quantile-based safety stock policies. These findings emphasize the critical role of end-to-end decision pipelines rather than algorithmic novelty alone, underscoring the value of predict-then-optimize workflows, decision alignment mechanisms, and governance artifacts—including calibration plots, cost curves, and threshold rationales—that ensure operating points remain auditable, interpretable, and resilient to model drift over time.
Copyright (c) 2022 Md Redwanul Islam, Md. Zafor Ikbal (Author)
GRAPH NEURAL NETWORK MODELS FOR DETECTING FRAUDULENT INSURANCE CLAIMS IN HEALTHCARE SYSTEMS
Fraudulent insurance claims in healthcare systems represent a persistent and costly challenge, undermining the efficiency, equity, and sustainability of healthcare delivery worldwide. Traditional approaches to fraud detection, including rule-based systems and statistical models, have provided valuable early insights but often fail to capture the complex, relational, and evolving nature of fraudulent behavior. This study addresses these limitations by investigating the application of Graph Neural Networks (GNNs) as an advanced analytical framework for detecting fraudulent claims. By representing patients, providers, and claims as interconnected nodes within graph structures, GNNs leverage relational dependencies and structural patterns that are frequently overlooked by conventional models. The research employed a mixed-methods design, combining quantitative experimentation with multiple GNN architectures—such as Graph Convolutional Networks, Graph Attention Networks, and GraphSAGE—with qualitative insights gathered from healthcare fraud investigators to evaluate interpretability and usability. The quantitative findings revealed that GNN models consistently achieved higher accuracy, precision, and recall compared to traditional classifiers, while the qualitative analysis highlighted the importance of interpretability and visualization in building stakeholder trust and improving investigative efficiency. Importantly, the literature review synthesized evidence from 112 peer-reviewed studies, providing a comprehensive overview of existing fraud detection methods and situating GNNs within the broader progression of healthcare fraud research. The results underscore that GNNs not only advance the technical accuracy of fraud detection but also offer practical tools for detecting collusive fraud networks and managing large-scale, heterogeneous claims data. This study contributes to both academic discourse and practical applications by demonstrating that GNNs are uniquely positioned to enhance fraud detection in healthcare insurance systems through their capacity to integrate relational learning, scalability, and operational transparency.
Copyright (c) 2022 Md. Tarek Hasan (Author)
QUANTITATIVE RISK ASSESSMENT OF RAIL INFRASTRUCTURE PROJECTS USING MONTE CARLO SIMULATION AND FUZZY LOGIC
Rail infrastructure programs frequently face complex, intertwined risks spanning cost overruns, schedule delays, safety concerns, and interface uncertainties. This systematic review critically examines how quantitative risk assessment (QRA) methods—Monte Carlo simulation (MCS), fuzzy logic (FL), and hybrid approaches—have been employed across the rail project lifecycle to manage these multidimensional challenges. Following PRISMA guidelines, we conducted a comprehensive search across Scopus, Web of Science, IEEE Xplore, ASCE Library, ScienceDirect, and TRB databases, applied pre-registered eligibility criteria, implemented double-screening for study inclusion, and rigorously appraised methodological practices encompassing data provenance, dependence modeling, validation, and sensitivity analysis. From an initial pool of studies, 95 peer-reviewed publications met all inclusion standards. Findings indicate MCS dominates (47%) owing to its strength in producing distributional forecasts and governance-ready percentiles; FL supports imprecise or linguistic inputs (33%) often encountered during early-stage planning and safety screening; while hybrid models (20%) bridge probabilistic propagation and evidential uncertainty, particularly in interface-intensive phases. Applications cluster within construction (68%), followed by design (46%), feasibility analysis (39%), testing/commissioning (24%), and operations & maintenance (21%). Methodologically, MCS studies primarily use triangular and PERT/beta distributions, with approximately 42% employing Latin hypercube sampling. However, dependence modeling remains limited—38% of studies assume independence, 23% employ rank or copula methods, and only 31% jointly simulate cost–schedule interactions. FL studies typically apply triangular/trapezoidal membership functions with centroid defuzzification; while two-thirds disclose reproducible rule bases, one-third lack transparency. Hybrid models frequently convert fuzzy assessments into probabilistic inputs or embed fuzzy and evidential nodes into Bayesian structures, enabling richer risk representation at system interfaces. Sensitivity analysis is reported in 64% of studies, but only 21% adopt global approaches and a mere 5% include tail-focused diagnostics, while external validation is rare (18%).
Copyright (c) 2022 Md. Sakib Hasan Hriday (Author)
QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP (QSAR) MODELING OF BIOACTIVE COMPOUNDS FROM MANGIFERA INDICA FOR ANTI-DIABETIC DRUG DEVELOPMENT
This study evaluates how quantitative structure–activity relationship (QSAR) modeling can accelerate anti-diabetic drug discovery from Mangifera indica (mango) phytochemicals. Diabetes mellitus remains a global health burden, and natural products represent an abundant but under-optimized resource for therapeutic leads. To address this gap, we conducted a comprehensive screening of major scientific databases and ultimately analyzed 113 peer-reviewed studies that reported computable molecular structures, harmonizable bioactivity endpoints, and reproducible modeling workflows. The review focused on clinically relevant antidiabetic targets including α-glucosidase, α-amylase, dipeptidyl peptidase-4 (DPP-4), and protein tyrosine phosphatase 1B (PTP1B). Across the included studies, curated datasets were normalized to pIC₅₀ and pKᵢ scales to enable meaningful comparisons, while feature engineering spanned physicochemical descriptors, topological indices, and molecular fingerprints such as ECFP and MACCS. Machine learning approaches ranged from penalized regression models to advanced ensemble algorithms (e.g., boosting, bagging, and kernel-based methods), with rigorous validation achieved through scaffold-aware data splits, external test sets, Y-randomization, and explicit applicability domain (AD) assessment. Convergent lines of evidence—including QSAR predictivity within AD, mechanistically plausible docking, and ADME/toxicity filtering—consistently highlighted polyphenolic chemotypes, particularly xanthones such as mangiferin and its aglycone norathyriol, as promising inhibitors of carbohydrate-metabolizing enzymes. In contrast, scaffolds targeting signaling enzymes (DPP-4 and PTP1B) demanded early consideration of selectivity, pharmacokinetics, and off-target liabilities to improve translational viability. This synthesis also identifies recurring optimism traps, such as reliance on internal-only validation, assay heterogeneity across studies, and insufficient reporting of AD boundaries. To mitigate these challenges, we propose a reproducible translational framework: (i) employ AD-bounded QSAR as a first-line triage tool to prioritize scaffolds, (ii) integrate orthogonal structure-based approaches such as docking and molecular dynamics for rationalization of binding interactions, and (iii) adopt permeability-aware optimization and formulation strategies to address polarity-driven bioavailability challenges inherent to many mango-derived polyphenols.
Copyright (c) 2022 Sadia Tasnim (Author)
EVALUATING THE EFFECTIVENESS OF AI-GENERATED, CONTEXT-SPECIFIC CASE STUDIES IN IMPROVING CRITICAL THINKING SKILLS IN ODL
The advancement of artificial intelligence (AI) in education has created new opportunities to enhance learner-centered pedagogies, particularly in open and distance learning (ODL) systems where geographic, cultural, and infrastructural challenges often limit instructional effectiveness. Critical thinking, defined as the ability to analyze, evaluate, and synthesize information for evidence-based judgment and problem-solving, remains a central educational outcome and a global competency emphasized by higher education frameworks. Case-based learning has long been recognized as an effective approach for fostering critical thinking; however, static case materials often lack contextual relevance and adaptability for diverse learner populations in ODL contexts. This study investigated the effectiveness of AI-generated, context-specific case studies in improving critical thinking skills among Bangladeshi ODL learners. A quasi-experimental design was employed with pre-test and post-test measures administered to an experimental group (n = 100) and a control group (n = 100). The experimental group interacted with AI-generated cases designed to provide personalization, authenticity, adaptive feedback, and active engagement, while the control group studied traditional instructor-developed case materials. Findings revealed significant improvements in the experimental group’s critical thinking performance, with large effect sizes on post-test scores compared to the control group. Regression analysis confirmed that personalization, engagement, authenticity, and adaptive feedback each had a strong positive effect on critical thinking outcomes, with path coefficients ranging between β = .27 and β = .46. Structural equation modeling further demonstrated that these four variables collectively explained 68% of the variance in critical thinking development, confirming the overall model hypothesis. The results underscore the effectiveness of AI-generated case studies in addressing key limitations of ODL, including isolation, lack of contextualization, and delayed feedback. By integrating AI-driven instructional design into learning management systems, institutions in Bangladesh can support critical thinking development at scale while ensuring inclusivity across diverse learner groups. This study contributes to the global literature on AI-enhanced pedagogy by demonstrating its applicability and impact within a South Asian ODL context, where access and equity are central educational challenges.
Copyright (c) 2024 Md Arif Uz Zaman (Author)
COST-BENEFIT ANALYSIS IN PRE-CONSTRUCTION PLANNING: THE ASSESSMENT OF ECONOMIC IMPACT IN GOVERNMENT INFRASTRUCTURE PROJECTS
This study presents a systematic literature review of 100 peer-reviewed, DOI-indexed articles examining the application of cost–benefit analysis (CBA) in the pre-construction stage of government infrastructure projects. Guided by the PRISMA framework, the review synthesizes evidence from transport, energy, water, and social infrastructure sectors to evaluate how welfare-consistent economic impact assessment is operationalized before major public investment decisions are finalized. Inclusion criteria required an explicit counterfactual (do-nothing or do-minimum baseline), monetized benefits and costs derived from willingness-to-pay or shadow-pricing methods, and clearly stated decision indicators including net present value (NPV), benefit–cost ratio (BCR), and internal rate of return (IRR) with a transparent price base and discount rate. The analysis explores eight thematic domains: welfare-consistent definitions and indicators, appraisal regimes and parameter libraries, baseline demand and exposure modeling, valuation of user benefits, safety, and environmental externalities, discounting and time horizons, uncertainty architecture, distributional analysis, and treatment of wider economic impacts. Findings reveal that transport dominates the literature at 52 percent, followed by energy at 20 percent, water at 18 percent, and social infrastructure at 10 percent. Most studies employ do-minimum baselines (84 percent) and constant real discount rates (72 percent) with growing adoption of declining schedules for long-lived assets. While deterministic sensitivity testing is universal, fewer studies employ probabilistic analysis (37 percent) or reference-class adjustments (21 percent). The review identifies strengths such as increasing parameter transparency, improved handling of induced demand, and avoidance of double counting as well as gaps in distributional weighting, probabilistic robustness, and integration of wider impacts. The synthesis offers a standardized reporting checklist and sector-specific recommendations to enhance transparency, comparability, and decision-readiness at the planning gate. By consolidating methodological best practices across jurisdictions, this review positions pre-construction CBA as a rigorous, auditable tool for ranking public infrastructure options on a welfare-consistent basis before design and procurement commitments become irreversible.
Copyright (c) 2022 Mubashir Islam, Abdul Rehman (Author)
ANALYSIS OF AI CONTRIBUTION TOWARDS REDUCING FUTURE PANDEMIC LOSS IN SME SECTOR: ACCESS TO ONLINE MARKETING AND YOUTH INVOLVEMENT
The COVID-19 pandemic imposed unprecedented operational disruptions on small and medium-sized enterprises (SMEs), with youth-led businesses emerging as one of the most vulnerable segments due to limited capital reserves, constrained digital infrastructure, and dependence on in-person transactions. Lockdowns, mobility restrictions, and shifts in consumer demand patterns accelerated the imperative for digital transformation, positioning artificial intelligence (AI) as a strategic enabler of business continuity, market adaptation, and resilience. This study investigates the role of AI-enabled tools—specifically customer interaction systems, predictive analytics, social media marketing automation, eCommerce enablement, and personalization—in supporting the survival and competitive positioning of youth-led SMEs during the pandemic. Findings reveal that AI-driven customer interaction tools maintained service continuity and reduced operational costs by up to 40%, with youth-led SMEs exhibiting greater adaptability in deploying low-code AI due to higher digital fluency. Cross-regional comparisons underscore disparities in adoption depth, shaped by infrastructural readiness, data quality, AI literacy, and regulatory frameworks. While developed economies leveraged advanced digital ecosystems for comprehensive AI integration, SMEs in developing regions often adopted modular, low-code solutions to circumvent skill and infrastructure constraints. The study also identifies persistent ethical and governance challenges—such as algorithmic bias, privacy risks, and uneven access—that were deprioritized in favor of immediate operational survival. These findings contribute to the growing body of literature on AI-enabled SME resilience by highlighting the unique absorptive capacities of youth-led enterprises, the synergistic effects of integrating multiple AI applications, and the contextual factors mediating adoption outcomes. The paper concludes by proposing a conceptual framework for future research, emphasizing the need for longitudinal, cross-sectoral, and policy-oriented studies to examine AI’s long-term impact on SME competitiveness in post-crisis economies.
Copyright (c) 2023 Danish Mahmud (Author)
MACHINE LEARNING-ENHANCED STATISTICAL INFERENCE FOR CYBERATTACK DETECTION ON NETWORK SYSTEMS
This study presents a comprehensive systematic review of 126 peer-reviewed publications on the integration of machine learning and statistical inference for cyberattack detection in network systems. The primary objective is to critically evaluate how adaptive computational models, when combined with probabilistic reasoning frameworks, enhance detection accuracy, interpretability, and operational efficiency in dynamic and evolving cyber threat landscapes. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the research process ensured transparency, methodological rigor, and replicability during literature identification, screening, and synthesis. The reviewed studies encompass a broad spectrum of machine learning paradigms—supervised, unsupervised, hybrid, and deep learning architectures—integrated with statistical inference methods such as Bayesian updating, likelihood estimation, hypothesis testing, probabilistic calibration, and statistical drift detection. Evidence consistently demonstrates that these integrated frameworks achieve superior true positive rates, reduced false positives, and greater resilience against zero-day and polymorphic attacks compared to traditional rule-based or signature-based systems. Notably, the studies highlight the pivotal role of dataset quality, diversity, and timeliness, with optimal results achieved when recent, representative data are combined with statistical preprocessing, dimensionality reduction, and adaptive feature selection techniques. Operational challenges in real-time deployment—such as minimizing latency, optimizing computational resources, and sustaining adaptability—are effectively addressed through innovations like lightweight statistical screening layers, adaptive thresholding, and distributed processing. Comparative experimental results further validate that integrated approaches deliver measurable improvements not only in technical detection metrics but also in scalability, cross-domain applicability, and human interpretability. This review concludes that the convergence of machine learning and statistical inference constitutes a mature, high-impact methodology for modern cybersecurity defense. However, it also identifies critical research gaps, including the absence of standardized performance benchmarks and limited validation in large-scale, real-world network environments. Addressing these gaps will be essential to ensuring the scalability, robustness, and long-term operational relevance of such integrated detection systems.
Copyright (c) 2022 Md.Kamrul Khan, Md Omar Faruq (Author)
REAL-TIME CYBERSECURITY INTEGRATION IN PLCS AND IOT GATEWAYS: A PERFORMANCE-CONSCIOUS APPROACH TO ENCRYPTION, AUTHENTICATION, AND INTRUSION DETECTION
The integration of real-time cybersecurity mechanisms into embedded systems has become a critical priority in the context of Industry 4.0, where programmable logic controllers (PLCs) and Internet of Things (IoT) gateways serve as foundational components of industrial automation. These devices are increasingly exposed to cyber-physical threats as they connect to broader networked infrastructures, yet their computational constraints and real-time operational requirements often preclude the use of conventional security measures. This study proposes and evaluates a performance-conscious cybersecurity framework designed to embed lightweight encryption, mutual authentication, and decentralized intrusion detection directly within industrial control devices. The research methodology involved the development of a modular security architecture tested on commercial-grade PLCs and IoT gateways, utilizing communication protocols such as Modbus/TCP, OPC UA, MQTT, and Profinet. Lightweight encryption algorithms, including SPECK, LEA, and ChaCha20, were implemented alongside elliptic curve-based authentication schemes to assess their latency, throughput, and system resource impact. Additionally, hybrid intrusion detection models—combining statistical baselining and anomaly detection—were deployed locally on embedded nodes to monitor network behavior in real time. The findings demonstrate that full-spectrum security can be achieved in embedded systems without disrupting deterministic control cycles. Encryption tasks remained within sub-millisecond boundaries, mutual authentication completed with minimal handshake delay, and intrusion detection maintained over 94% accuracy with low false positives. Resource utilization across CPU, memory, and power remained within device tolerances, and the architecture sustained operational stability during prolonged testing. The study confirms that embedded cybersecurity, when implemented with algorithmic efficiency and system-aware scheduling, can coexist with mission-critical automation processes. This work challenges the conventional reliance on perimeter defenses and post-factum monitoring, offering instead a secure-by-design approach that integrates security functions natively within embedded industrial systems. The proposed framework supports scalable deployment, protocol interoperability, and long-term resilience, advancing the field toward real-time, embedded protection for critical infrastructure environments.
Copyright (c) 2025 H M Shamsuzzaman, MD Mosleuzzaman, Mohammad Shah Paran (Author)
CYBERCRIME, LEGAL ACCOUNTABILITY, AND CONTRACTUAL RISK: A SYSTEMATIC REVIEW OF JURISPRUDENCE AND PROTECTIVE FRAMEWORKS
This systematic review investigates the evolving legal intersection between cybercrime and contractual liability, with a focus on how courts, regulators, and contracting parties address cybersecurity risks through enforceable legal frameworks. Drawing upon the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, a total of 87 peer-reviewed articles, case law commentaries, and legal-technical policy studies representing over 1,300 cumulative citations were rigorously analyzed to identify prevailing legal doctrines, risk mitigation practices, and enforcement trends. The review explores key thematic areas including the rise of cybersecurity-specific clauses in digital service agreements, the misalignment between cyber insurance policies and commercial contracts, the legal treatment of third-party vendor breaches, and the contractual implications of data protection regulations such as the GDPR, CCPA, and HIPAA. Findings reveal a clear doctrinal shift: courts are increasingly recognizing cybersecurity failures as breaches of contract, especially when they violate performance warranties or industry standards. Furthermore, vague or boilerplate clauses have proven ineffective during litigation, underscoring the importance of specificity and alignment with technical benchmarks such as NIST and ISO/IEC standards. The review also identifies a growing reliance on Data Processing Agreements (DPAs), Standard Contractual Clauses (SCCs), and enforceable indemnity and audit rights to manage legal risk in complex digital ecosystems. High-profile cases such as Merck v. ACE Insurance, Schrems II, and the British Airways GDPR enforcement illustrate how regulatory action and private litigation are catalyzing more rigorous contract drafting and cyber risk governance. Overall, the study concludes that in the face of rising transnational cyber threats, contractual instruments must evolve beyond static legal templates to become dynamic tools of compliance, risk transfer, and strategic cybersecurity management. This review offers both scholars and practitioners a synthesized, evidence-informed framework for understanding and improving the legal mechanisms that govern cyber contractual liability.
Copyright (c) 2024 Md Nazrul Islam Khan, Md Soyeb Rabbi (Author)
CUSTOMER RELATIONSHIP MANAGEMENT AND DATA-DRIVEN DECISION-MAKING IN MODERN ENTERPRISES: A SYSTEMATIC LITERATURE REVIEW
In an era where digital transformation and customer-centricity drive competitive differentiation, the integration of Customer Relationship Management (CRM) systems with Data-Driven Decision-Making (DDD) practices has emerged as a critical enterprise capability. This study presents a systematic literature review and meta-analysis of empirical research conducted between 2010 and 2024, synthesizing findings from 78 peer-reviewed articles across disciplines including marketing, information systems, and organizational science. The primary objective was to evaluate the effectiveness of CRM-DDD integration on organizational performance and to identify functional, sectoral, and contextual factors influencing these outcomes. A rigorous search strategy, guided by PRISMA standards, was used to extract eligible studies from five major academic databases. Meta-analytic procedures were performed using a random-effects model to account for heterogeneity across industries, geographies, and CRM configurations. The analysis revealed a statistically significant and moderately strong overall effect size (r = 0.46), affirming that CRM systems embedded with analytics capabilities lead to superior customer satisfaction, retention, marketing ROI, and strategic responsiveness. Among CRM functionalities, analytical CRM demonstrated the highest impact (r = 0.52), followed by collaborative (r = 0.44) and operational CRM (r = 0.37), indicating that insight generation and cross-functional alignment are central to maximizing CRM value. Sectoral analyses showed that CRM-DDD integration yields the greatest benefits in retail, finance, and healthcare, while also delivering measurable gains in B2B and industrial environments. Moderator analyses further revealed that effect sizes were stronger in developed economies, large enterprises, and organizations with advanced digital maturity. Robust statistical diagnostics confirmed the stability and replicability of findings, while theoretical triangulation linked results to the Resource-Based View, Knowledge-Based View, and relationship marketing theories. This study contributes to both academic literature and managerial practice by offering a comprehensive, evidence-based understanding of CRM analytics as a performance-enhancing capability. It underscores the strategic imperative for organizations to not only adopt CRM platforms but to embed them with advanced data-driven intelligence and integrate them across the customer lifecycle to unlock sustained competitive advantage.
Copyright (c) 2024 Tahmina Akter Rainy, Md Arifur Rahman, Anika Jahan Mou (Author)
IN SILICO DRUG REPURPOSING FOR INFLAMMATORY DISEASES: A SYSTEMATIC REVIEW OF MOLECULAR DOCKING AND VIRTUAL SCREENING STUDIES
The escalating global burden of inflammatory diseases—marked by persistent immune dysregulation, multisystem involvement, and complex molecular etiologies—has intensified the need for innovative therapeutic strategies that minimize cost, reduce development timelines, and increase success rates. Drug repurposing, the practice of identifying new therapeutic uses for existing drugs, has emerged as a strategic alternative to de novo drug discovery, particularly through in silico methodologies such as molecular docking, virtual screening, and cheminformatics-guided candidate selection. This systematic review synthesizes and evaluates recent advancements in computational repurposing approaches aimed at inflammatory disorders, including rheumatoid arthritis, inflammatory bowel disease, psoriasis, and systemic lupus erythematosus. Following the PRISMA 2020 guidelines, a comprehensive literature search was conducted across multiple scientific databases—including PubMed, Scopus, Web of Science, Embase, and IEEE Xplore—to identify peer-reviewed studies published between January 2010 and April 2022. A total of 65 articles met the inclusion criteria, encompassing diverse in silico workflows that examined drug-target interactions using molecular docking platforms such as AutoDock, AutoDock Vina, Schrödinger’s Glide, MOE, and GOLD, often combined with ADMET profiling tools (e.g., SwissADME, pkCSM) and molecular dynamics simulations to validate binding stability. Target proteins of interest commonly included pro-inflammatory mediators such as TNF-α, IL-6, IL-1β, JAK1/2, and NF-κB, with FDA-approved kinase inhibitors and anti-cancer drugs frequently emerging as high-affinity binders suitable for cross-disease application. In addition, the review documents methodological convergence in scoring thresholds, ligand library design, and reproducibility standards across computational studies. Several case studies demonstrate successful downstream validation of in silico predictions via in vitro or in vivo assays, reinforcing the translational potential of these approaches. However, key challenges persist, including lack of consensus on docking protocol standardization, limited exploration of off-target toxicities, and insufficient integration with systems pharmacology and biological network modeling. This review concludes that in silico drug repurposing represents a rapidly evolving, resource-efficient approach for identifying new treatments in immunopathology, but emphasizes the need for hybrid computational-experimental pipelines and improved benchmarking to realize its full clinical utility.
Copyright (c) 2022 Mansura Akter Enni, Md Abdul Ahad Maraj (Author)
THE ROLE OF PERCEIVED ENVIRONMENTAL RESPONSIBILITY IN ARTIFICIAL INTELLIGENCE-ENABLED RISK MANAGEMENT AND SUSTAINABLE DECISION-MAKING
As artificial intelligence (AI) becomes increasingly embedded in sustainability governance, understanding the social and ethical contexts that influence its deployment is critical. This study investigates the role of Perceived Environmental Responsibility (PER) as a central construct in moderating stakeholder trust, enhancing AI adoption, and improving the performance of AI-enabled risk management systems in environmentally sensitive decision-making. While AI offers capabilities such as predictive analytics, real-time monitoring, and environmental impact simulations, its effectiveness is often shaped by how stakeholders interpret the intentions and values of the organizations that deploy these technologies. This research explores the hypothesis that PER not only influences stakeholder acceptance of AI but also determines organizational readiness, transparency outcomes, and alignment with sustainability objectives. Using a systematic meta-analysis approach guided by PRISMA methodology, the study synthesizes findings from 122 peer-reviewed articles published between 2010 and 2024. The findings reveal that PER functions as both a moderating and mediating variable: it amplifies trust in AI-based decisions, enhances stakeholder interpretability of complex models, and improves strategic integration of AI into environmental, social, and governance (ESG) frameworks. Organizations with high PER demonstrated stronger performance in AI-based compliance reporting, emissions management, and environmental decision-making, while those with low PER experienced reputational risk, stakeholder skepticism, and underutilization of AI tools. The study contributes to the literature on stakeholder theory, responsible innovation, and sustainable technology adoption by positioning PER as a foundational variable in ethical AI integration. It also aligns PER with the principles of the Triple Bottom Line (TBL) and ESG reporting, emphasizing its operational and reputational importance in digital sustainability governance. By synthesizing evidence across sectors and regions, the research provides actionable insights for organizations seeking to implement AI responsibly. It concludes that PER is not simply a passive perception but an active enabler of technological legitimacy, ethical alignment, and long-term sustainability success in AI-supported decision environments.
Copyright (c) 2024 Subrato Sarker, Md. Nuruzzaman (Author)
BEHAVIORAL FACTORS IN LOAN DEFAULT PREDICTION A LITERATURE REVIEW ON PSYCHOLOGICAL AND SOCIOECONOMIC RISK INDICATORS
This systematic review investigates the psychological and socioeconomic risk indicators that influence loan default behavior, aiming to bridge the gap between traditional credit assessment models and emerging behavioral insights. As financial institutions increasingly face challenges in accurately predicting borrower defaults, it becomes crucial to explore non-traditional variables such as cognitive biases, personality traits, financial literacy, income volatility, and employment stability. Drawing on a comprehensive synthesis of 67 peer-reviewed studies published between 2010 and 2024, this review analyzes a wide range of empirical evidence from diverse geographical, cultural, and lending contexts. The findings indicate that behavioral factors particularly impulsivity, time-inconsistency, and overconfidence play a critical role in undermining repayment discipline, especially when compounded by limited financial literacy and socioeconomic instability. Moreover, the review highlights the growing use of behavioral interventions, such as personalized nudges, commitment devices, and financial education tools, which have shown measurable effectiveness in reducing default rates. The integration of behavioral analytics into credit risk assessment, as seen in emerging hybrid models, represents a shift toward more holistic, personalized, and accurate prediction frameworks. Additionally, the review underscores the importance of tailoring financial products and risk models to cultural and contextual realities, particularly in underserved markets. By synthesizing interdisciplinary research across economics, psychology, and finance, this study provides a comprehensive framework for understanding the multidimensional drivers of loan default and offers strategic insights for lenders, policymakers, and fintech innovators aiming to enhance creditworthiness assessment and borrower support systems.
Copyright (c) 2024 Abdullah Al Maruf, Md Masud Kowsar, Mohammad Mohiuddin, Hosne Ara Mohna (Author)
A REVIEW OF AI-POWERED DATA VISUALIZATION IN ENTERPRISE REPORTING: DASHBOARD DESIGN AND INTERACTIVE ANALYTICS
The increasing complexity and volume of data within modern enterprises have significantly elevated the importance of advanced, intuitive, and adaptive visualization tools for reporting and analytics. This study systematically reviews and conducts a meta-analysis of existing research on artificial intelligence (AI)-powered data visualization dashboards, with a specific emphasis on their design, interactivity, and analytical effectiveness within enterprise contexts. It critically evaluates the role of AI technologies—including predictive analytics, anomaly detection, and natural language processing—in enhancing dashboard usability, interpretability, scalability, and responsiveness. By integrating findings from diverse industry sectors, including healthcare, finance, public administration, and humanitarian organizations, the analysis demonstrates consistent positive impacts of AI-enhanced dashboards on user satisfaction, decision-making efficiency, and cognitive load reduction. The synthesis highlights best practices in adaptive interface design, emphasizing personalized visualization strategies that dynamically respond to user roles and organizational functions. Furthermore, systematic benchmarking and rigorous comparative analysis illustrate dashboards' substantial value in maintaining competitive advantage and facilitating organizational agility. Critical challenges identified include ensuring algorithmic transparency, maintaining high data quality, and addressing infrastructure constraints for scalability. Overall, the findings offer robust evidence that strategic, user-centered implementation of AI-driven dashboards significantly enhances organizational decision-making capabilities, operational efficiency, and user engagement. This extended review contributes valuable insights for practitioners and researchers aiming to leverage AI technologies effectively in enterprise analytics, emphasizing continuous improvement through benchmarking, transparent algorithmic practices, and targeted user-oriented design methodologies.
Copyright (c) 2022 Sadia Zaman, Mohammad Ariful Islam, Sumyta Haque, Hosne Ara Mohna (Author)
IMPLEMENTING ADVANCED TECHNOLOGIES FOR ENHANCED CONSTRUCTION SITE SAFETY
The construction industry remains one of the most hazardous sectors worldwide, consistently reporting high rates of workplace accidents, injuries, and fatalities. Traditional safety management approaches, while essential, are often reactive and limited in their ability to predict and prevent incidents. As the industry evolves, there is an urgent need for a paradigm shift toward more proactive and technology-driven safety strategies. This study investigates the implementation of advanced technologies—including the Internet of Things (IoT), Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), unmanned aerial vehicles (drones), and wearable safety devices—to significantly enhance safety outcomes on construction sites. IoT-enabled sensors can continuously monitor environmental conditions such as temperature, noise, air quality, and structural integrity, offering real-time data for early detection of potential hazards. AI algorithms further analyze this data to identify patterns, predict risks, and automate responses before accidents occur. VR and AR provide immersive training experiences and on-site hazard visualization, helping workers to recognize and respond to dangerous situations with higher accuracy and preparedness. Drones offer aerial surveillance capabilities, allowing for safe inspection of hard-to-reach or hazardous areas. Meanwhile, wearable technologies such as smart helmets, vests, and biometric monitors track workers’ vital signs, movements, and proximity to danger zones, enabling real-time alerts and safety interventions. Collectively, these technologies represent a transformative approach to construction site safety by shifting from manual and fragmented safety processes to integrated, data-driven, and responsive systems. The integration of these tools not only reduces the probability of human error but also enhances compliance with safety regulations and improves overall situational awareness. The anticipated outcome of this project is a substantial reduction in injury and fatality rates, along with the development of more robust and proactive safety protocols tailored to the dynamic nature of construction environments. Through the adoption of smart technologies, the construction industry can advance toward a safer, more efficient, and future-ready operational model.
Copyright (c) 2025 Md Ismail Hossain, Md Mahfuj Hosen, Mohammad Aman Ullah Sunny, Shofiul Azam Tarapder (Author)
DETERMINANTS OF WORKFORCE EFFICIENCY: AN ANALYSIS OF EMPLOYEE PERFORMANCE DRIVERS IN JIANGSU'S AUTOMOTIVE SECTOR
The rapid expansion of China’s automotive sector, particularly in Jiangsu Province, underscores the need to identify key drivers of workforce efficiency to sustain productivity and competitive advantage. This study examines the impact of leadership style, supervision, work discipline, and motivation on employee performance within Jiangsu’s automotive industry. Drawing on existing literature, we posit that these factors critically influence workforce productivity and operational outcomes. Adopting a quantitative approach, primary data were collected through structured surveys administered to 410 employees, with rigorous adherence to ethical guidelines. Our analysis reveals that leadership style, supervision, work discipline, and motivation exhibit statistically significant effects on employee performance (p < 0.05). While external factors remain influential, the findings demonstrate that targeted management practices can enhance workforce efficiency. The study contributes actionable insights for industry practitioners, suggesting that optimizing these determinants can improve performance metrics. For future research, we recommend investigating the mediating roles of organizational culture, technological integration, and macroeconomic conditions to develop adaptive strategies for sustained productivity growth. Results are presented through descriptive and inferential statistics, with empirical support for all hypothesized relationships.
Copyright (c) 2025 Chen Xinjian, Mohamad Idrakisyah (Author)
A STUDY OF BUSINESS PROCESS AUTOMATION WITH DEVOPS: A DATA-DRIVEN APPROACH TO AGILE TECHNICAL SUPPORT
The convergence of Business Process Automation (BPA) and DevOps represents a transformative shift in enterprise operations, aiming to bridge the gap between process efficiency and continuous software delivery. This systematic literature review investigates the role of BPA-DevOps integration in enhancing agile technical support systems, operational agility, and organizational scalability. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a total of 147 peer-reviewed scholarly articles published between 2014 and 2024 were meticulously selected from six prominent academic databases. The selected studies span a range of domains, including IT services, SaaS, healthcare, finance, and public sector applications, offering a comprehensive cross-sectoral perspective. The review identifies that the synergistic adoption of BPA and DevOps contributes to streamlined deployment pipelines, automated incident resolution, enhanced customer support responsiveness, and measurable performance improvements such as reduced change failure rates and faster lead times. Notably, mature automation environments were found to yield greater consistency, resilience, and scalability across organizational functions. However, the review also surfaces critical research gaps—particularly the lack of unified implementation frameworks, inconsistent performance measurement indicators, limited empirical assessments of long-term automation impacts, and insufficient evaluation of ethical and socio-cultural considerations. Furthermore, the study emphasizes the pivotal role of organizational culture, leadership engagement, role redefinition, and continuous skills development in ensuring successful BPA-DevOps adoption. Through thematic synthesis, the review advances the academic discourse by integrating technical, operational, and managerial dimensions, while also advocating for future research that prioritizes inclusivity, longitudinal evaluation, and domain-specific adaptability. The insights generated from this review serve as a foundational reference for both scholars and practitioners seeking to design, implement, and assess sustainable, scalable, and value-aligned automation strategies within DevOps ecosystems.
A SYSTEMATIC REVIEW OF PUBLIC BUDGETING STRATEGIES IN DEVELOPING ECONOMIES: TOOLS FOR TRANSPARENT FISCAL GOVERNANCE
This systematic review investigates the evolution, implementation, and effectiveness of public budgeting strategies in developing economies, with a particular focus on transparency, equity, and institutional accountability. Drawing on a total of 94 peer-reviewed articles and high-quality institutional studies published between 2015 and 2025, the study examines the adoption and outcomes of key budgeting reforms, including participatory budgeting, performance-based budgeting, gender-responsive budgeting, and donor-supported fiscal initiatives. Employing the PRISMA 2020 guidelines, the review followed a rigorous multi-stage process involving identification, screening, eligibility assessment, and synthesis, resulting in a thematically structured evaluation of reform models across diverse regional contexts. The findings demonstrate that while budgeting innovations such as participatory budgeting in Sub-Saharan Africa and Latin America, performance-based budgeting in Southeast Asia, and gender-responsive budgeting in South Asia have produced measurable improvements in service delivery and transparency, their effectiveness remains uneven and highly context-dependent. Key barriers to sustained reform include limited administrative capacity, fragmented inter-ministerial coordination, weak political will, and the pervasive influence of informal institutions such as clientelism and patronage networks. Furthermore, donor-driven reforms, while often effective in catalyzing change, frequently suffer from limited local ownership and post-project sustainability challenges. The review also identifies critical gaps in the existing literature, including a dearth of longitudinal impact assessments, insufficient multi-country comparative analyses, and limited empirical work on the integration of digital platforms for budget transparency and citizen engagement. The study contributes to the growing body of public financial management scholarship by offering a comparative, evidence-based synthesis that highlights both the opportunities and systemic constraints shaping fiscal governance in the Global South. Ultimately, the review underscores the need for adaptive, politically aware, and institutionally grounded approaches to public budgeting reform that align with local governance realities and long-term development goals.
Copyright (c) 2025 Sazzad Islam (Author)
AI INTEGRATION IN E-COMMERCE BUSINESS MODELS: CASE STUDIES ON AMAZON FBA, AIRBNB, AND TURO OPERATIONS
This systematic review explores the integration of Artificial Intelligence (AI) within contemporary e-commerce business models by analyzing a total of 126 peer-reviewed scholarly articles published between 2013 and 2023. The review aims to provide a comprehensive understanding of how AI technologies such as machine learning, deep learning, natural language processing, and predictive analytics are being utilized to enhance operational performance, customer engagement, and strategic decision-making across major e-commerce platforms. Drawing on case studies from industry leaders such as Amazon, Airbnb, and Turo, the study investigates core applications of AI in areas including personalized recommendation systems, dynamic pricing algorithms, automated logistics and supply chain management, fraud detection, customer service automation, and trust-building mechanisms in peer-to-peer marketplaces. The findings reveal that AI significantly contributes to improving service accuracy, responsiveness, and scalability, while also reinforcing trust and reducing operational risks through autonomous systems. Furthermore, the study discusses the divergent implementation of AI across centralized and decentralized e-commerce models, illustrating the contextual variability in system design and use. The review also addresses the growing influence of international regulatory frameworks, such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and other jurisdictional mandates, which are increasingly shaping AI governance practices. Key gaps in the literature are identified, including the underrepresentation of small-to-medium enterprises (SMEs), the lack of cross-industry comparative analyses, and limited longitudinal studies on the socio-economic and ethical implications of AI integration. By synthesizing current evidence and highlighting both opportunities and limitations, this review contributes to a deeper academic and practical understanding of AI’s transformative role in e-commerce, offering valuable insights for researchers, policymakers, and digital commerce stakeholders.
NAVIGATING ETHICS AND RISK IN ARTIFICIAL INTELLIGENCE APPLICATIONS WITHIN INFORMATION TECHNOLOGY: A SYSTEMATIC REVIEW
Advancements in artificial intelligence (AI) have profoundly transformed a wide array of sectors, with Information Technology (IT) standing at the forefront of this revolution. AI technologies have reshaped IT operations by introducing new levels of automation, predictive capabilities, decision-making precision, and efficiency, resulting in sweeping changes across organizational infrastructures and service delivery models. However, alongside these technological breakthroughs, the integration of AI has surfaced numerous ethical concerns and associated risks that warrant critical and comprehensive exploration. Issues such as algorithmic bias, privacy breaches, lack of transparency, accountability dilemmas, and cybersecurity vulnerabilities remain pervasive, posing significant challenges for organizations seeking to deploy AI responsibly. Recognizing these complexities, this study focuses on examining the ethical challenges, risk factors, and essential considerations surrounding the deployment of AI within IT environments. This research addresses the urgent and growing necessity for organizations to adopt robust ethical frameworks and effective risk management strategies to ensure that AI integration promotes fairness, transparency, and accountability. A comprehensive synthesis of existing literature further enriches this foundation by offering diverse perspectives on the ethical and risk-related challenges posed by AI, highlighting both the transformative potential and the vulnerabilities associated with its integration into IT systems. Through this review, key gaps in the current body of knowledge are identified, particularly regarding the practical implementation of ethical standards and risk mitigation strategies across varied organizational contexts. Adopting a qualitative research methodology, the study employs a case study approach to explore the intricate, multifaceted issues involved in AI integration within IT operations. This methodological choice allows for a nuanced understanding of real-world scenarios, organizational behaviors, and stakeholder dynamics related to AI deployment. Data collection is meticulously based on trustworthy sources, including peer-reviewed academic journals, authoritative industry reports, regulatory and governmental publications, and credible news articles, ensuring the comprehensiveness and reliability of insights gathered. By triangulating data across multiple domains, the study captures a holistic view of the ethical landscape surrounding AI in IT. Finally, the research culminates with an in-depth interpretation of findings, accompanied by practical recommendations and implications. These outcomes aim to contribute to the development of ethical, sustainable, and responsible AI integration practices within the IT industry, supporting organizations in navigating the complex interplay between technological innovation and ethical accountability.
Copyright (c) 2025 Ishtiaque Ahmed (Author)
COMPARATIVE ANALYSIS OF NEURAL NETWORK ARCHITECTURES FOR MEDICAL IMAGE CLASSIFICATION: EVALUATING PERFORMANCE ACROSS DIVERSE MODELS
Medical image classification has become a critical task in computer-aided diagnosis, enabling faster and more accurate detection of diseases such as cancer, pneumonia, and diabetic retinopathy. This study presents a comprehensive comparative analysis of prominent neural network architectures—Convolutional Neural Networks (CNNs), Residual Networks (ResNets), DenseNets, Vision Transformers (ViTs), and EfficientNets—in the context of medical image classification. Utilizing benchmark datasets including ChestX-ray14, ISIC Skin Cancer, and Retinal Fundus Images, we evaluated each model's performance based on accuracy, precision, recall, F1-score, training efficiency, and robustness to overfitting. The results demonstrate that while CNN-based models like ResNet and DenseNet maintain strong classification capabilities with balanced computation cost, ViTs outperform others in high-resolution image interpretation, especially under complex feature distributions. EfficientNet offers a trade-off between speed and accuracy, making it suitable for resource-constrained clinical settings. Our findings highlight the architectural strengths and weaknesses in varied medical imaging scenarios, providing insights into the selection of optimal models based on diagnostic goals, dataset characteristics, and computational resources..
Copyright (c) 2024 Md Tawfiqul Islam, Md Anikur Rahman, Md. Tanvir Rahman Mazumder, Shahadat Hossain Shourov (Author)
A SYSTEMATIC REVIEW OF CREDIT RISK ASSESSMENT MODELS IN EMERGING ECONOMIES: A FOCUS ON BANGLADESH'S COMMERCIAL BANKING SECTOR
This systematic literature review explores the evolution, application, and performance of credit risk assessment models in emerging economies, with a focused lens on Bangladesh’s commercial banking sector. In an environment marked by institutional constraints, limited data infrastructure, and evolving regulatory frameworks, selecting the appropriate credit risk model is critical for financial stability and inclusion. Drawing from a total of 98 peer-reviewed studies published up to 2022, this review synthesizes evidence from academic and applied research to evaluate traditional statistical models—such as logistic regression and discriminant analysis—as well as machine learning approaches including support vector machines, decision trees, and neural networks. The review follows the PRISMA 2020 guidelines to ensure transparency, replicability, and methodological rigor throughout the review process. Key findings indicate that while machine learning models consistently outperform traditional models in terms of predictive accuracy, they are rarely adopted at scale due to concerns about model interpretability, regulatory acceptance, and institutional readiness. Furthermore, the review identifies major gaps in sector-specific model development, integration of alternative and real-time data, and post-deployment performance monitoring. The synthesis reveals that most models are designed generically, with limited adaptation to specific industries such as garments, agriculture, SMEs, and microfinance, thereby reducing their predictive relevance in context. Additionally, institutional barriers including lack of analytical expertise, fragmented IT infrastructure, and vague regulatory guidelines hinder the operationalization of advanced credit risk tools. The findings emphasize the necessity of aligning model sophistication with contextual realities, and the importance of balancing predictive performance with explainability and institutional capacity. This review offers an evidence-based foundation for policymakers, banking professionals, and researchers seeking to develop more inclusive, accurate, and operationally viable credit risk models in emerging-market financial ecosystems.
Copyright (c) 2022 Md Masud Kowsar (Author)