Stroke remains one of the leading causes of mortality and long-term disability worldwide, creating substantial clinical, economic, and social burdens. Despite significant advances in prevention and treatment, the increasing prevalence of cardiovascular diseases, aging populations, and lifestyle-related risk factors continue to elevate stroke incidence across diverse populations. Traditional stroke risk assessment approaches rely primarily on statistical models and clinician-based evaluations; however, these methods often struggle to capture complex nonlinear interactions among multiple risk determinants. The emergence of machine learning (ML) has introduced innovative opportunities for improving predictive accuracy, enabling personalized risk assessment, and supporting proactive healthcare interventions. This systematic review critically examines the role of machine learning–driven frameworks in predicting stroke risk factors, synthesizing evidence from contemporary studies addressing epidemiological trends, clinical determinants, predictive analytics, and computational modeling approaches. The review analyzes existing machine learning methodologies, including nonlinear predictive systems, cardiovascular risk-based frameworks, and healthcare analytics models applied to stroke prediction. Furthermore, it evaluates the strengths, limitations, and practical implications of ML-based risk prediction systems within clinical environments. Findings indicate that machine learning models consistently outperform conventional risk stratification approaches in handling multidimensional datasets, identifying hidden relationships among variables, and supporting individualized prediction. However, challenges related to data quality, model interpretability, generalizability, and clinical integration remain significant barriers to widespread adoption. The review proposes a conceptual framework integrating epidemiological, clinical, and computational dimensions to strengthen future stroke prediction systems. The study contributes to the growing literature on intelligent healthcare analytics by providing a comprehensive assessment of machine learning applications for stroke risk prediction and identifying future directions for research and implementation.

Modern enterprises are increasingly dependent on scalable data analytics systems capable of handling large, heterogeneous, and rapidly changing datasets. While cloud-based big data platforms dominate this landscape, spreadsheet environments—particularly Microsoft Excel enhanced with Power Pivot—continue to play a critical role in enterprise reporting due to their accessibility, flexibility, and widespread adoption. This paper examines the scalability of spreadsheet-based analytics with a focus on Microsoft Excel Power Pivot as a semantic modeling and in-memory analytics engine. It evaluates how Power Pivot extends traditional spreadsheet limitations by integrating relational data modeling, in-memory compression, and advanced calculations using DAX. The study situates Power Pivot within the broader ecosystem of big data analytics, comparing it conceptually with distributed frameworks such as MapReduce and Spark-based systems. It also explores governance, data integration, and time-series analytics challenges in enterprise environments. Drawing from established literature in business intelligence, data governance, machine learning, and distributed computing, the paper argues that Excel Power Pivot remains a relevant and scalable solution for mid-tier enterprise analytics when properly architected. The discussion highlights trade-offs between usability and scalability, and proposes hybrid architectures combining spreadsheet models with enterprise-grade data pipelines.

The modernization of enterprise banking systems has become a strategic requirement as financial institutions increasingly depend on high-availability, low-latency, and secure digital services. Traditional monolithic banking applications often struggle to support real-time transaction processing, elastic scalability, fault isolation, and rapid feature delivery. This research paper proposes a cloud-native, event-driven enterprise banking platform designed using Java microservices, Kafka-based asynchronous communication, secure API gateways, automated DevOps pipelines, and infrastructure-as-code practices. The study develops a conceptual and technical architecture for transforming legacy banking systems into distributed real-time ecosystems capable of handling payments, account services, fraud monitoring, notification workflows, audit logging, and regulatory reporting.

The proposed model is grounded in prior research on serverless and cloud-native computing, function composition, platform performance, fraud detection, anomaly identification, and distributed enterprise security. Existing literature highlights the advantages of cloud programming abstraction, scalable service deployment, and event-based processing, while also revealing unresolved challenges related to latency, observability, false positives in financial monitoring, and operational complexity (Jonas et al., 2019; Baldini et al., 2017; Castro et al., 2019; Wang et al., 2018). In response, this paper presents an integrated architecture that combines domain-driven microservice decomposition, Kafka event streams, Java-based service orchestration, CI/CD automation, containerized deployment, and real-time analytics pipelines.

The findings indicate that event-driven microservices can improve transaction responsiveness, reduce dependency coupling, and enhance system resilience when compared with tightly integrated legacy architectures. Kafka enables durable, scalable, and replayable event processing, while automated deployment pipelines reduce release friction and support continuous modernization. However, the model also introduces governance challenges involving event schema management, distributed tracing, data consistency, security enforcement, and operational monitoring. The study concludes that cloud-native event-driven banking platforms offer significant potential for mission-critical financial modernization, provided that performance engineering, fraud controls, DevSecOps, and compliance-by-design principles are embedded into the platform architecture.

 Modern enterprises operate in highly dynamic environments where decision-making depends on the seamless integration of presentation systems, analytics platforms, and operational intelligence tools. Despite the availability of advanced software ecosystems such as Microsoft PowerPoint, Power BI, and Redzone, organizations often struggle to unify narrative communication, real-time analytics, and shop-floor operational visibility into a single coherent decision-support framework. This paper proposes an integrated enterprise intelligence framework that connects presentation intelligence, business analytics, and operational execution layers. The objective is to enhance decision speed, accuracy, and traceability across organizational hierarchies.

The framework leverages Microsoft PowerPoint as a structured narrative layer for executive communication, Power BI as a data-driven analytical engine, and Redzone as an operational intelligence system for frontline workforce performance monitoring. Drawing insights from data hiding techniques, optimization methods, and intelligent system design principles (Bender et al., 1996; Gielen and Rutenbar, 2000; Clerc, 2002), the proposed model ensures that insights are not only generated but effectively communicated and operationalized.

The paper also explores how advanced computational methods such as particle swarm optimization (Kennedy and Eberhart, 1995) and statistical optimization (Medeiro et al., 1994) can enhance analytics pipelines within Power BI, while structured communication models inspired by information hiding and watermarking techniques (Petitcolas et al., 1999; Johnson et al., 2001) improve data integrity in enterprise reporting. The study concludes that integrated intelligence systems significantly improve enterprise agility, reduce decision latency, and enhance operational efficiency.

Large-scale data analysis has become a critical requirement in modern business intelligence systems due to exponential growth in structured and semi-structured data. Traditional spreadsheet tools are often insufficient for handling massive datasets efficiently, leading to performance bottlenecks, delayed computations, and limited scalability. Microsoft Excel Power Pivot, combined with in-memory data modeling, provides a powerful solution for optimizing performance in analytical environments. It enables efficient data compression, faster query execution, and advanced analytical capabilities through the VertiPaq engine and relational data modeling structures. This technical paper explores the architecture, optimization strategies, and performance enhancement techniques associated with Power Pivot, focusing on its integration with business intelligence workflows. It also discusses real-world applications in enterprise reporting, predictive analytics, and decision support systems. The study highlights how in-memory processing significantly reduces computational overhead and enhances scalability compared to traditional disk-based systems. Furthermore, comparisons are drawn with distributed computing frameworks such as MapReduce and Spark to contextualize the role of Excel-based modeling in modern analytics ecosystems. The findings suggest that Power Pivot, when properly optimized, serves as a highly efficient tool for medium to large-scale analytical workloads, bridging the gap between end-user analytics and enterprise-level data processing systems.