International Journal of Research and Applied Innovations
Welcome to IJEETR The International Journal of Engineering & Extended Technologies Research (IJEETR) is a prestigious, peer-reviewed, open-access international journal committed to fostering innovation, collaboration, and excellence in research across the broad spectrum of engineering and technological sciences. In a rapidly evolving world where technology is reshaping every facet of human life, IJEETR strives to be more than just a journal – it is a knowledge hub where scholars, academicians, researchers, and industry practitioners come together to exchange pioneering ideas, transformative research, and ground breaking solutions to global challenges. We believe in bridging the gap between theory and practice, encouraging interdisciplinary collaboration, and ensuring that innovative discoveries reach a worldwide audience with maximum impact. Why Choose IJEETR? Publishing with IJEETR offers you: Rigorous Peer Review Global Reach Timely Publication Indexing & Abstracting Interdisciplinary Collaboration Impact-Oriented Dissemination IJEETR is currently accepting manuscripts for its upcoming issue. We encourage submissions that present: Novel methodologies and experimental results. Innovative frameworks and technological applications. Critical reviews of contemporary research trends. Submit your manuscript to editor@ijeetr.com and be a part of a global network of innovators shaping the future of engineering and technology. Call for Papers Volume 7, Issue 4, July-August 2025 Deadline: 31st August 2025 Notification: 3 to 4 Days Publication: 24 to 36 Hours E-Certificates: 6 Hours Contact Us For manuscript submission, queries, or collaboration opportunities: Email: editor@ijeetr.com Website: www.ijeetr.com Support: +91-9710583466
Designing Cloud-Native Enterprise Systems by Modernizing Applications with Microservices and Kubernetes Platforms
The growing need to scale, make enterprise systems efficient and flexible has led to the implementation of cloud-native architecture, micro-service and containerization platforms such as Kubernetes. In this research article, the authors investigate how cloud-native enterprise systems are designed through the upgrading of the legacy application using microservices and their implementation on Kubernetes platforms. It is a detailed architecture of how to re-architecture monolith applications into a distributed system based on microservices that are more scalable, maintainable and have fault tolerance. The paper discusses major aspects of this modernization process, e.g., service decomposition, containerization, orchestration, and deployment strategies. It also emphasizes the use of Kubernetes to orchestrate the containers, which allows automatic scaling, self-healing and deployment of microservices. The article provides case studies and real life examples to illustrate how this approach has been effective in the enterprise world setting. Other issues that the research touches upon are the issues of data consistency, security and the difficulties of the administration of distributed systems. Finally, the work is a useful source of information to organizations that need to modernize their applications and be cloud-native to remain competitive in the rapidly changing world of technology
A Risk-Aware Generative AI and LLM-Driven Cloud Framework for Secure Banking with PII Protection and Privacy Analytics in 5G Web Applications
The rapid evolution of digital banking, cloud-native systems, and 5G-enabled web applications has increased the need for secure, privacy-aware, and intelligent financial platforms. At the same time, the widespread use of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) has created new opportunities for advanced analytics, automation, and customer engagement. However, the integration of these technologies also introduces risks related to personally identifiable information (PII) exposure, data misuse, and regulatory non-compliance. This paper proposes a risk-aware Generative AI and LLM-driven cloud framework designed to support secure banking operations while ensuring PII protection and privacy analytics in 5G web environments. The framework integrates cloud-native infrastructure, real-time data pipelines, and LLM-based decision intelligence with a multi-layered privacy and security architecture. It incorporates encryption, anonymization, differential privacy techniques, and zero-trust access control to safeguard sensitive financial and customer data. A privacy analytics layer enables continuous monitoring of PII usage, consent management, and regulatory compliance with standards such as GDPR and financial data protection policies. Generative AI models support fraud detection, customer support automation, and predictive analytics while operating under strict governance policies to prevent data leakage and model bias The proposed architecture was evaluated using simulated banking transaction datasets and privacy-sensitive user interaction data. Results demonstrate improved detection of anomalous activities, enhanced privacy risk visibility, and reduced latency in 5G-enabled cloud environments. The integration of LLM-driven analytics with privacy-aware controls improved decision-making accuracy while maintaining data confidentiality and regulatory compliance. The study highlights the importance of combining risk-aware AI governance, privacy analytics, and secure cloud infrastructure to build trustworthy digital banking ecosystems. The framework provides a scalable and adaptable approach for financial institutions seeking to leverage generative AI while protecting sensitive customer information in next-generation web applications
Cross Domain AI and Secure API Gateway Based Cloud Native Platforms for Enterprise Decision Making and Real Time Data Intelligence
Enterprises increasingly rely on cloud-native platforms to integrate data and services across heterogeneous domains such as finance healthcare mobile applications and digital ecosystems. Secure and scalable access to these distributed resources is critical as application programming interfaces serve as the primary interaction layer. This paper proposes a cross-domain AI-driven cloud-native platform built around secure API gateways to support enterprise decision making and real-time data intelligence. The architecture embeds artificial intelligence within the API layer to enable intelligent traffic management anomaly detection and adaptive access control while supporting high-bandwidth data exchange. Cloud-native microservices and event-driven pipelines provide elastic scalability and resilience for real-time analytics. The proposed approach improves security posture reduces response latency and enhances decision accuracy compared to traditional rule-based gateway and monolithic architectures. The framework offers a practical blueprint for building secure intelligent and interoperable enterprise platforms.
Service Mesh Implementation Strategies for Zero-Downtime Migrations in Production Environments
In this paper, service mesh technologies are outlined with reference to strategies of implementation, namely, implementation of zero-downtime migrations in production settings. The study examines the potential of service meshes like Istio to be used to control the communication of the microservices such that the system remains resilient during the tricky transitions. The architecture presented here focuses on state-of-the-art traffic management solutions, including canary deployments and blue-green deployments, in order to make the rollout of new versions of the services or new infrastructure changes gradual and risk-free. The platform guarantees the efficient routing of traffic between the old and new version of the services to reduce chances of downtimes or interruption of services. It is observable, and real-time monitoring and tracing are part of the service mesh, which monitors the health, performance, and user impact of the services in the midst of migrations. Secure communication is enabled with the help of mutual TLS between microservices to ensure data confidentiality and integrity of data throughout the migration process. The framework emphasizes the dynamism of controlling and monitoring traffic flows, which allows to control granulometrically the deployment process. Through such service mesh strategies, companies are able to have their smooth, zero-downtime migrations, which will improve the stability of production environments and ensure that high service availability and security standards are maintained
Modern Automation Strategies for Biomedical Research Infrastructures: A Technical Framework
The need for biomedical research institutions is to have digital infrastructures that can ensure security, reproducibility, and sustained regulatory compliance, and at the same time facilitate rapid scientific innovative processes. To deal with these problems in a setting where sensitive health information is processed, the National Institutes of Health and National Library of Medicine have established extensive automation models that are specifically targeted to combat these issues. The architecture combines Infrastructure-as-Code, container orchestration, and compliance-based workflow in accordance with the Health Insurance Portability and Accountability Act and Federal Information Security Management Act requirements. The frameworks of Machine Learning Operations have proven that it is possible to achieve a high degree of automation of infrastructure in a systematic way that leads to a considerable decrease in the cost of operations and, at the same time, ensures high-quality standards. Using continuous validation pipelines, system configuration drift was reduced substantially, and release frequency increased multiplefold without audit deviation. The deployment unified biomedical workflows across multiple institutes while ensuring data integrity through automated encryption rotation. Organizations implementing MLOps practices report substantial improvements in deployment reliability and model governance across diverse computing environments. This model demonstrates that automation, when embedded with regulatory intelligence, can enable scalability and security simultaneously. This framework introduces a public-trustfocused automation model that demonstrates automation, when embedded with regulatory intelligence, can enable scalability and security simultaneously. Scholarly significance arises from bridging automation theory and biomedical informatics practice, establishing a replicable framework for modernization in lifescience computing environments. The principles of design of the framework, such as infrastructure-ascode, ongoing compliance validation, and intrinsic regulatory intelligence, give a blueprint that can be adopted in various institutional settings and regulatory frameworks
Designing Hybrid Cloud and Big Database Architectures for High Availability and Cost Efficiency
Enterprises increasingly rely on data-intensive applications that demand high availability, fault tolerance, and predictable performance while operating under strict cost constraints driven by competitive markets and regulatory pressures. Traditional on-premise database infrastructures continue to offer strong control over data locality, security, and compliance requirements, yet they are often constrained by limited elasticity, long procurement cycles, and high capital expenditures that inhibit rapid scaling. In contrast, public cloud platforms enable near-instant provisioning, geographic distribution, and elastic resource utilization, but introduce concerns related to long-term operational costs, data sovereignty, and governance complexity when used exclusively. This article presents a hybrid cloud database and big database architecture that strategically integrates private and public cloud resources to balance these trade-offs, achieving high availability and cost efficiency simultaneously. By leveraging distributed NoSQL systems such as MongoDB, Apache Cassandra, and DataStax Enterprise alongside hybrid deployment patterns, the proposed architecture supports scalable analytics, resilient data storage, and workload-aware data placement across heterogeneous environments. Drawing on established research and practical architectures published between 2000 and 2017, this study synthesizes design principles, evaluates architectural and economic trade-offs, and proposes a reference model that guides enterprises in designing robust, flexible, and economically sustainable hybrid big database deployments.
AI-Powered API Management: Intelligent traffic routing and adaptive scaling
The integration of artificial intelligence into API management marks a transformative advancement in cloud computing infrastructure. As organizations increasingly adopt microservices architectures, traditional static API management approaches prove insufficient to handle the exponential growth in traffic volume and complexity. This paradigm shift introduces intelligent automation across critical dimensions of API operations, including traffic routing, resource allocation, and security monitoring. By establishing dynamic behavioral baselines and employing predictive analytics, these AI-powered systems can anticipate demand fluctuations, optimize resource distribution, and identify emerging security threats with unprecedented accuracy. The transition from reactive to proactive management enables organizations to avoid performance degradation during peak periods while simultaneously reducing infrastructure costs through precise resource allocation. The continuous learning capabilities inherent in these systems ensure ongoing improvement without manual intervention, effectively addressing the complexity challenges introduced by microservices proliferation. This fundamental advancement promises to reshape enterprise digital service delivery, unlocking significant value through enhanced performance, efficiency, and reliability in cloud environments.
Accessibility-Centered Mobile Architectures for Government Health Initiatives
Digital access is essential in securing health equity by means of government health mobile platforms, in which the inclusion of users across diverse groups is essential. The paper evaluates the concept of integrating accessibility-based mobile architecture with the significance of considering the accessibility capabilities of the system, i.e., WCAG compliance, assistive technology, and adaptive UI design directly at the system-level architecture, as opposed to secondary. With accessibility being aligned with the essential aspects of the mobile platform design, these platforms will be able to cater to the needs of the elderly user, individuals with disabilities, and low literacy populations better, making them more engaged and inclusive. This would be a proactive measure to make sure that the mobile applications are created in such a way that they are usable since it is in the design stage. The success of accessibility-first architecture has been demonstrated with real-world case studies, involving the increased adoption of the technology by users, a decrease in the abandonment rate, and an increase in trust in large-scale public health applications, such as clinical research platforms. Such conclusions indicate that accessibility in the form of a primary aspect of mobile architecture is associated with making vulnerable groups more successful in adopting them and with implementing health initiatives in general. In this way, the inclusive design approach can make the public health platforms formidable health equity representation, equitable access to vital health information and services, and to ensure that the needs of diverse communities are fulfilled by the digital era.
Reliable and Secure SDN/NFV-Based 5G Cloud Networks for AI-Powered Healthcare, Fraud Detection, and Industrial Analytics
The rapid deployment of 5G networks and cloud-based infrastructures has accelerated the adoption of artificial intelligence (AI) in healthcare, financial fraud detection, and industrial analytics. However, traditional network architectures face challenges in meeting stringent requirements for reliability, security, scalability, and low latency demanded by these mission-critical applications. Software-Defined Networking (SDN) and Network Function Virtualization (NFV) offer programmable and flexible network management capabilities that can address these challenges when integrated with cloud and AI technologies. This paper presents a reliable and secure SDN/NFV-based 5G cloud network framework designed to support AI-powered healthcare services, real-time fraud detection, and intelligent industrial analytics. The proposed architecture enables dynamic traffic management, adaptive security enforcement, and efficient resource utilization while ensuring data privacy and service continuity. AI-driven analytics enhance predictive decision-making, anomaly detection, and operational intelligence across heterogeneous environments. The framework is evaluated conceptually across healthcare, financial, and industrial use cases, demonstrating its ability to improve reliability, reduce latency, mitigate cyber threats, and support large-scale deployments. The study highlights the importance of integrated network intelligence and security for next-generation digital ecosystems.
AI Enabled SAP Decision Intelligence using Machine Learning for Secure Cloud Platforms and Financial Risk Detection with Fault Tolerant Integration Architectures
The rapid adoption of cloud-based enterprise systems has increased the demand for intelligent, secure, and resilient decision-making platforms. SAP Decision Intelligence, when enhanced with artificial intelligence (AI) and machine learning (ML), provides organizations with the capability to transform large-scale enterprise data into actionable insights. This paper explores an AI-enabled SAP Decision Intelligence framework deployed on secure cloud platforms to enhance financial risk detection and operational decision-making. Machine learning models are leveraged to identify anomalies, predict financial risks, and support real-time decision automation while maintaining data integrity and compliance. The study further emphasizes fault-tolerant integration architectures that ensure system reliability, high availability, and seamless interoperability across heterogeneous cloud environments. By combining advanced ML techniques, secure cloud infrastructure, and robust integration patterns, the proposed approach improves risk mitigation, scalability, and resilience in modern financial and enterprise systems
AI-Enabled Enterprise Transformation through Predictive Analytics and Cyber Threat Intelligence with Human–AI Collaboration in Secure Cloud Environments
AI-enabled enterprise transformation has become a strategic imperative as organizations seek to enhance decision-making, security resilience, and operational efficiency in increasingly complex digital environments. This paper presents an integrated framework for enterprise transformation through predictive analytics and cyber threat intelligence with human–AI collaboration in secure cloud environments. The proposed approach combines advanced machine learning models for forecasting business and operational outcomes with real-time cyber threat intelligence to proactively identify, assess, and mitigate security risks. Human–AI collaboration is embedded across analytical and governance layers to ensure explainability, ethical oversight, and domain-informed decision support. Secure cloud architectures provide scalability, interoperability, and resilience while enabling privacy-preserving data processing across distributed enterprise systems. The framework supports multi-industry use cases including financial services, claims management, supply chain optimization, and critical enterprise operations. By unifying predictive intelligence, cybersecurity analytics, and collaborative AI workflows, the proposed model enables enterprises to achieve data-driven transformation while maintaining trust, compliance, and security. This work contributes a holistic perspective on aligning AI-driven analytics with human expertise and secure cloud infrastructures to support sustainable and resilient enterprise modernization.
Optimizing Oracle Databases Through Multi Cloud and Hybrid Cloud Strategies: A Framework for Scalability, Resilience, and Cost Efficiency
The paper analyses the optimization of the Oracle databases in multi-cloud and hybrid environment to enhance effectiveness in their scaling, resilience, and cost-effectiveness. It based it on a qualitative review of scholarly articles, technical reports and actual enterprise cases. The results reveal that such technologies as Oracle Sharding, Data Guard, and Autonomous Database can be used to enhance performance and minimize downtime. Another key point described by the paper is that the AI-based orchestration and containerization simplify the process of management in various cloud platforms. The findings offer a guideline on how the enterprises can incorporate the sensible Oracle database strategies in hybrid cloud scenarios with reasonable performance and reliability.
Transparent and Auditable Decision-Making in Enterprise Platforms
The growing use of automated decision-making platforms within the enterprise settings becomes a major challenge in terms of transparency and auditability. These platforms typically work as black boxes, and it is hard to justify the results, justify the rightness of the decision and correspond to the audit requirements. This paper suggests a design-based approach of transparency where automated decisions are just artifacts of the system. The architecture suggested also makes sure that the decisions can be traced by recording structured traces of decisions, assessments of constraints, policy contextual metadata and rationale summaries of every outcome. This practice will assist various stakeholders, such as engineers, operators, auditors and the leadership team at the same time making data secure and the system performance viable. This is achieved by directly incorporating transparency into the core of the platform and providing organizations with the potential to have traceable, defensible, and reviewable processes of scale. The paper also presents some of the practical design factors which should be in place in implementing audit-ready decision systems that can be applied in real-life enterprise set-ups. Finally, the suggested framework will provide more accountability, traceability, and auditability in automated decision systems and ensure that enterprise platforms are reliable, fair, and consistent with regulatory requirements.
Secure and Scalable Financial Intelligence Systems Using Big Data Analytics in Hybrid Cloud Environments
The article looks into the development of scalable and safe finance intelligence infrastructure in the hybrid cloud environment, which is grounded on the big data analytics. The degree of security and scalability when entering a large volume of data is highly important in the modern world where the financial sphere highly depends on digital technologies. To manage these issues, the framework proposed in this study will be the integration of big data analytics with cloud computing that would provide a comprehensive degree of data protection, real-time processing opportunities, and scalability. The proposed system will enhance the data security and processing power because the hybrid cloud environments provide the flexibility of the public cloud and the control and privacy of the private clouds. The system will also include an architecture of the elements of data storage, data processing, and analytics that will be backed up by the secure encryption protocols and access control measures to keep sensitive financial information under lock and key. This framework also makes scalability straightforward because the system is able to support increased loads of data without compromising its performance as well as its security. The paper also has the details of integrating AI and machine learning into predictive analytics and can be applied in making real-time decisions and in evaluating risks. The article highlights the most important aspect of regulatory compliance, data privacy laws and use of advanced encryption software to secure financial information on the cloud. It is a supplement to the field because it gives an all-encompassing model that financial institutions that desire to progress their analytical framework might employ without losing their level of security and compliances.
The Role of Cloud-Native Architecture in Enabling Sustainable Digital Infrastructure
This article examines the critical intersection of cloud architecture and environmental sustainability within enterprise technology solutions. As organizations face increasing pressure to reduce environmental impacts while maintaining competitive digital capabilities, emerging as prominent promoters of cloud-indigenous architectural patterns of resource adaptation. Discussion shows how specific cloud patterns, adaptation tools, and architectural decisions contribute to energy consumption and carbon emissions. Through examination of serverless computing, auto-scaling mechanisms, and robust observability frameworks, the article illuminates pathways through which technical architecture can align with broader sustainability imperatives. Cloud architects occupy a position of significant responsibility in facilitating the transition toward environmentally sustainable digital ecosystems while enabling continued innovation and performance in increasingly complex distributed systems.
Designing Accessibility-First Enterprise Web Platforms at Scale
With the growth of enterprise web platforms to reach a variety of people in industries such as education, health, and financial services, access has been a post-hoc matter instead of a design-level consideration. The framework presented in this paper is a framework of accessibility-first engineering adapted to large-scale enterprises on a web platform, in which the concept of inclusivity is built into system design, software development, and delivery chains. The discussion explores the compatibility of the technologies, such as semantic HTML, ARIA standards, keyboard navigation, and assistive technology, into complex and dynamic web applications, without affecting the performance and scalability. Such essential architectural considerations of implementing accessibility to server-rendered and client-side interfaces are discussed as content updates in real time and component-based design systems. Production scenarios described in case studies indicate the existence of tangible benefits, including enhanced usability, reduced accessibility defects, and increased access by people with disabilities. The article highlights the role played by accessibility-first in creating digital platforms that are not just compliant but also fair, strong, and user-friendly by all users, irrespective of their capabilities. This study is based on the shift in thinking about the web development of an enterprise and the concept of accessibility as a central architectural concern, not the peripheral issue.
Feedback-Driven Runtime Adaptation for Synchronization Primitives
Modern multithreaded applications operate under highly dynamic execution conditions, where contention patterns on shared data structures vary significantly over time due to phase changes, workload skew, and platform-specific scheduling effects. Conventional synchronization primitives—such as pure spin locks, queue-based locks, or blocking mutexes—are static by design and therefore optimized for only a narrow operating regime. As a result, developers are forced to choose a single synchronization strategy that may perform well in one phase but poorly in another. This paper presents Feedback-Driven Runtime Adaptation for Synchronization Primitives, a practical framework that treats synchronization as a closed-loop control problem. Rather than proposing a new lock algorithm, we continuously observe lightweight contention telemetry, estimate runtime pressure, and adapt synchronization behavior using simple, explainable policies augmented with explicit stability constraints. We instantiate the framework for a mutex-like primitive in portable user-space C++ and evaluate it on a modern ARM-based Apple M1 system under non-stationary workloads. Experimental results demonstrate that feedback-driven adaptation induces bounded and meaningful behavioral changes under contention, trading peak throughput for improved predictability and robustness while avoiding oscillatory behavior. The results confirm that controlled runtime adaptation provides a viable alternative to static synchronization strategies in modern systems.
Automating Compliance in Biomedical DevOps: A Policy-as-Code Approach
Governance in regulated biomedical contexts has become more and more reliant on automated mechanisms of governance in order to maintain compliance, security and auditability of swiftly changing software delivery ecosystems. This study shows a Policy-as-Code (PaC) model which includes regulatory criteria into biomedical DevOps pipelines via machine executable, readable policy definitions. The architecture proposed allows compliance controls to be versioned, tested, and enforced with the application code and infrastructure settings to keep them aligned with the regulatory requirements at all times. The framework (which is practiced in the federal biomedical research systems) illustrates the way in which PaC supports real-time compliance verification and automated control enforcement, and the creation of immutable audit artifacts throughout the software lifecycle. It enables to promote conformity in regulatory frameworks, such as NIST SP 800-53, FISMA, and HIPAA, lessening the use of manual reviews and periodic audits. Empirical testing indicates that configuration drift has reduced significantly, the authorization cycles have decreased and compliance overhead is quantifiably reduced without affecting the security or performance of the system. Achieving this by formalizing regulatory requirements by making digital policies a continuously assessed digital policy, the study redefines compliance as an inherent system property and not a post hoc system checking process. The results demonstrate the importance of PaC in promoting secure CI/CD processes, allowing a continuous authorization mechanism, and enhancing governance automation of well-regulated biomedical settings. This publication provides a scaling model of regulatory engineering and provides realistic considerations in the implementation of compliance-aware DevSecOps architectures in public-sector and biomedical cloud systems.
API-Driven Account Onboarding Framework with Real-Time Compliance Automation
In this study, the proposed API-based digital account onboarding framework is introduced, and it is implemented in terms of MuleSoft-based integration patterns, which are used to optimise the onboarding process. The framework supports important operations like identity checking, customer data checking, and real-time compliance checks using reusable APIs and event-on-command workflows. Computerised regulatory implementation and centralised record keeping enhance audit preparation and lessen the duration required to finish the onboarding process. This paper illustrates the application of API-led connectivity to develop a scalable, safe and regulatory onboarding system that meets the needs of the regulated enterprise setting. Efficiency is afforded in both the validation requirements in the integration, and it minimises risks of manual errors and loss of time. The framework supports the increasing demand for high-speed and safe digital onboarding with a high degree of regulatory compliance in highly-regulated industries, by focusing on automation and API-based connectivity. The architecture is a tactical method to update the enterprise systems, which allows businesses to improve the onboarding processes without compromising security and compliance. The study makes valuable contributions to the research on the role of API-led integration to enhance the efficiency of operations and customer experience in regulated industries.
Designing Intelligent Enterprise Architectures Integrating Generative AI with Cyber Defense and Data Driven Decision Frameworks
Enterprises are increasingly dependent on digital platforms to support strategic decision-making, operational efficiency, and competitive advantage. At the same time, escalating cyber threats and the growing complexity of data ecosystems challenge the effectiveness of traditional enterprise architectures. This paper proposes an intelligent enterprise architecture that integrates generative artificial intelligence with cyber defense mechanisms and data driven decision frameworks. The proposed architecture unifies advanced analytics, generative AI reasoning capabilities, and security-aware data pipelines to enable proactive, adaptive, and resilient decision intelligence. Generative AI is leveraged not only for predictive insights but also for scenario simulation, automated reasoning, and contextual explanation, while cyber defense components ensure continuous monitoring, threat detection, and risk mitigation. The framework embeds governance, transparency, and human oversight to support trustworthy and compliant AI adoption. By aligning decision intelligence with cybersecurity and architectural governance, the proposed approach enables enterprises to transition from reactive defense and fragmented analytics toward intelligent, integrated, and resilient enterprise systems. This research contributes a conceptual and methodological foundation for next-generation enterprise architectures capable of supporting secure, data driven, and AI-augmented decision-making across complex digital environments.
Predictive Monitoring for Distributed and Relational Database Management Systems (RDBMS): A Comprehensive Analysis
Predictive monitoring represents a paradigm shift in database management that transforms reactive maintenance practices into proactive optimization strategies. Traditional database monitoring systems detect problems only after they manifest, resulting in costly downtime and performance degradation. Predictive monitoring leverages machine learning algorithms, statistical analysis, and time-series forecasting to anticipate system failures, performance bottlenecks, and resource limitations before they impact end users. Both distributed databases and relational database management systems benefit from predictive capabilities that enable early detection of node failures, query performance degradation, and hardware malfunctions. Implementation strategies encompass automated scaling mechanisms, resource optimization algorithms, and capacity planning procedures that respond to predicted workload changes. Real-world applications demonstrate significant improvements in system availability, cost reduction through proactive maintenance, and enhanced customer experiences across e-commerce platforms, financial transaction systems, and healthcare data management environments. Advanced monitoring perspectives integrate with automation tools to create self- healing systems that perform root cause analysis and continually improve models. The operational benefits include substantial cost savings, minimized downtime, and optimized performance metrics. Strategic advantages encompass enhanced decision-making capabilities, competitive positioning through system reliability, and improved customer satisfaction. The evolution toward predictive database monitoring enables organizations to maintain high-performance, reliable systems that support critical business operations while reducing administrative overhead and infrastructure costs.
Blockchain Technology, Big Data, and Government Policy as Catalysts of Global Economic Growth
This paper examines three key factors that significantly influence the global economy: blockchain technology, big data, and government policy. The first section explores blockchain technology, including its historical development, fundamental working principles, and applications beyond cryptocurrency. It also analyzes how blockchain enhances transparency, efficiency, and trust, thereby contributing to global economic growth. The second section focuses on big data, explaining its definition, core characteristics, and the ways it improves organizational decision-making, efficiency, and innovation. Additionally, it discusses the future potential of big data and its expanding role in driving economic development worldwide. The third section highlights the importance of government and policy in strengthening the global economy by examining the government’s role in economic regulation and stability. It provides examples of existing policies and proposes additional measures that governments can implement to promote sustainable economic growth.
A Study and Exploration of Numerous Impacts in Children’s Literature: Conditioned Discrimination
A group’s diversification can be observed and experienced in terms of its varied ethnicity, class, race, religion, language, colour, gender of the group members. But it is also perceived that discrimination based on many factors is created in society. As a social taboo, gender inequality is considered to be an idea and a situation that, women and men are not equal. It is also a grave thing that this conditioned discrimination is deep-rooted. It may be socially constructed or empirically grounded. Gender difference shows and promotes unequal treatment to or perceptions of individuals just because of their gender. It arises from variances in socially constructed gender roles which unfortunately produces different responsibilities. In India, this gender discrimination refers to health, education, economic and political inequalities between men and women. It influences women's health, their educational achievement, economic conditions and India's sex ratio. In literature, such discrimination is evident since long. Through the theme of gender issues, it discusses inhuman mindset based on gender prevailing in the society and furthers some effective solutions to check such conditioned discrimination.
A Unified AI and Cloud Security Model for Financial Fraud Prevention and Medical Image Intelligence in 5G-Powered Web Applications
The rapid integration of artificial intelligence (AI), cloud computing, and 5G networking is enabling transformative solutions across industries. This study proposes a unified AI and cloud security model that combines financial fraud prevention and medical image intelligence within 5G-powered web applications. The framework leverages cloud-native security features and AI-driven analytics to detect, prevent, and respond to threats in real time. In financial systems, machine learning models analyze transaction patterns, user behavior, and network anomalies to identify fraudulent activities, reducing financial losses and improving trust. In healthcare, deep learning algorithms interpret medical images such as X-rays, CT scans, and MRIs, enabling faster and more accurate diagnoses. The 5G-enabled web application layer ensures high-speed data transfer, low latency, and reliable access for mobile and IoT devices, supporting telemedicine and remote financial services. The unified model emphasizes secure data storage, end-to-end encryption, identity management, and compliance with privacy regulations. By integrating AI, cloud security, and 5G technologies, the proposed model provides a scalable, interoperable, and resilient platform that enhances fraud prevention and medical intelligence. The framework also supports real-time monitoring and automated response mechanisms, contributing to safer and more efficient digital ecosystems.
AI-Enabled Cloud Lakehouse for Large-Scale Data Warehousing: SAP Integration for Secure Analytics and Tableau-Driven Reporting
The rapid growth of enterprise data across transactional systems, IoT platforms, cybersecurity logs, and analytical applications has intensified the need for scalable, secure, and intelligent data warehousing architectures. Traditional data warehouses struggle to accommodate heterogeneous data formats, real-time analytics, and advanced artificial intelligence (AI) workloads. This paper presents an AI-enabled cloud lakehouse framework for large-scale data warehousing that integrates SAP enterprise systems with advanced security analytics and Tableau-driven reporting. The proposed architecture combines the flexibility of data lakes with the governance and performance of data warehouses, enabling unified analytics across structured, semi-structured, and unstructured data. AI and machine learning models are embedded within the lakehouse to support secure analytics, fraud detection, and anomaly identification, while SAP integration ensures consistency with enterprise transactional data. Tableau is employed as a visualization layer to provide dynamic, interactive, and role-based reporting for decision-makers. Experimental analysis and enterprise use-case evaluations demonstrate that the proposed framework improves data accessibility, analytics performance, security visibility, and decision accuracy compared to conventional warehouse-centric approaches. The framework is particularly suitable for data-intensive enterprises seeking secure, scalable, and insight-driven analytics in cloud environments.
Event-Driven BI Pipelines for Operational Intelligence in Industry 4.0
The emergence of Event-Driven Business Intelligence (BI) pipelines is an innovative operational intelligence approach in Industry 4.0 because it allows real-time decision-making by processing all types of dynamic and diversified data. These pipelines are created to compute and analyse the events as they happen and enable real-time insights and responses to businesses that need to function in a highly connected and data-heavy environment. An Event-Driven BI pipeline is often designed based on event producers, an event processing engine, data storage systems and analytics modules connected by an event bus or message broker. The ongoing emissions of events by event producers, IoT devices, sensors and enterprise systems are captured and processed in real time. The event processing engine has the role of filtering, enriching, and aggregating events, and finally stores them in the data warehouses or distributed storage systems. The pieces of information stored are then used by analytics and BI dashboard tools, which analyse the business data to produce operational insights. The key points of the architecture include the processing of event streams, real-time data ingestion, and data storage, wherein the low-latency and high-throughput processing are guaranteed. It can be described that the Industry 4.0 systems have enhanced operational efficiency, predictive maintenance, and better resource management through the implementation of Event-Driven BI pipelines in them. The paradigm shift lets businesses be active to new challenges, and opportunities provide it with a competitive advantage in the digital era.
Design of a Secure SAP-Enabled Cloud Framework Using Generative AI and GANs for Healthcare Incrementality Analytics
The increasing adoption of data-driven decision-making in healthcare has intensified the need for secure, scalable, and intelligent analytical frameworks capable of accurately measuring intervention effectiveness. This paper proposes the design of a secure SAP-enabled cloud framework that leverages Generative Artificial Intelligence (AI) and Generative Adversarial Networks (GANs) to support incrementality analytics in healthcare systems. The proposed architecture integrates heterogeneous healthcare data sources within a cloud-native SAP environment, enabling secure data ingestion, governance, and high-performance analytics. Generative AI models are employed to simulate counterfactual scenarios, while GANs enhance data augmentation and bias mitigation for robust incrementality testing. Security is enforced through a zero-trust approach incorporating encryption, identity-aware access control, and continuous compliance monitoring to address regulatory requirements such as HIPAA and GDPR. Experimental analysis demonstrates improved accuracy in treatment impact assessment and campaign effectiveness evaluation compared to traditional statistical approaches. The framework provides a scalable foundation for trustworthy and intelligent healthcare analytics in cloud-based enterprise environments.
An Intelligent Risk-Aware AI and LLM Platform for Secure Banking Operations and Trade Safety Analytics in Cloud-Based Web Applications
The rapid digitization of banking operations and trade systems has increased efficiency, accessibility, and global financial integration, but it has also amplified the risks of fraud, cyberattacks, and operational anomalies. Traditional detection systems are often static, reactive, and unable to handle high-velocity, multi-source data in real time. This paper proposes an intelligent risk-aware AI and Large Language Model (LLM) platform for secure banking operations and trade safety analytics within cloud-based web applications. The platform integrates generative and predictive AI models with LLMs to identify anomalies, forecast potential threats, and generate human-readable explanations and automated reports. Secure Extract, Transform, Load (ETL) pipelines ensure consistent, high-quality data from heterogeneous sources, while a risk-aware module dynamically evaluates threat severity, prioritizes mitigation, and adjusts system parameters. Cloud-native deployment enables scalable, low-latency, and fault-tolerant analytics suitable for high-speed web applications. Evaluation on real and simulated banking and trade datasets demonstrates improved detection accuracy (>95%), a 35–40% reduction in false positives, and enhanced operational resilience. This work provides a blueprint for deploying adaptive, interpretable, and secure AI platforms that integrate risk-awareness, cloud scalability, and 5G-ready web technologies to safeguard modern financial ecosystems.
Enhancing Healthcare Financial Decision-Making using Secure AI-Enabled SAP Cloud Platforms
Healthcare organizations increasingly rely on data-driven financial systems to manage costs, ensure regulatory compliance, and improve operational efficiency. The integration of Secure AI-Enabled SAP Cloud platforms offers a robust solution for enhancing healthcare financial decision-making by combining advanced analytics, automation, and enterprise-grade security. This approach leverages artificial intelligence to analyze large volumes of financial, clinical, and operational data in real time, enabling accurate forecasting, fraud detection, revenue cycle optimization, and strategic planning. Cloud-based SAP architectures provide scalability, reliability, and interoperability while ensuring compliance with healthcare regulations such as HIPAA and data protection standards. Security mechanisms including identity access management, encryption, and continuous monitoring further protect sensitive financial and patient data. By unifying finance, analytics, and security within an intelligent SAP cloud ecosystem, healthcare organizations can achieve improved transparency, faster decision cycles, and sustainable financial performance. The proposed framework highlights how secure AI-driven SAP platforms can transform healthcare financial management into a resilient, efficient, and insight-driven process.
Robust Supply Chain Systems in Cloud-Distributed Environments: Design Patterns and Insights
Global supply-chain platforms run at unparalleled scale as geographically dispersed networks are being operated with continuous volatility in their operations. To be able to design systems that can be depended upon in such circumstances, there must be architectural methods that take into consideration failure, inconsistency, and disruption as regular functions deserving of consideration instead of rare accidents. The paper will analyze the architectural designs that are required to construct resilient supply-chain platforms within distributed cloud environments. It conceptualizes the modern supply chains as complex adaptive systems that are vulnerable to partial service failures, asynchronous data propagation, as well as real-time external shocks, e.g. demand fluctuations, logistics delays and infrastructure outages. The paper compares significant design patterns, such as scopes of limited transactions, compensation-style and saga-style workflows, and fault-isolation boundaries which restrict the scope of failures in services. Besides, the paper highlights other concepts of resilience engineering, like graceful degradation, strategic redundancy, and recovery-first, that focus on quick stabilization rather than consistency. Combining these architectural patterns and real-life constraints experienced in large-scale supply-chain operations, the paper will offer a systematic platform for designing platforms that maintain continuity, observability, and reliability. The results can provide concrete recommendations to architects and engineers who want to create cloud-native supply-chain systems that would remain operational even in the face of ongoing uncertainty and unforeseeable disruptions.
, Fault tolerance Compensation-based workflows