International Journal of Engineering & Extended Technologies Research (IJEETR)
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. Our Vision & Mission · Vision: To be recognized globally as a leading platform for the dissemination of high-quality, innovative, and impactful research in engineering and extended technologies. · Mission: o To promote a culture of scientific excellence and innovation. o To provide open access to research, ensuring equitable knowledge sharing. o To accelerate the application of research findings into real-world practices. o To foster collaboration between academia, industry, and society.
Leveraging AI and Cloud Technology for Disaster Prediction and Management
Cloud Computing and Artificial Intelligence (AI) are two of the widely used technological domains in the present world. Clouds are considered as a base for Disaster Management Support Systems. In the cloud environment, people have enormous computing, storage, and communication capabilities at ease so that they could collect large amounts of data from different sources when disaster incidents occur, and later analyze these data and build knowledge. Data and information serve as the critical backbone of Disaster Management. AI techniques and systems are used in Disaster Management area for Disaster prediction and monitoring. AI is useful for searching damaged buildings and planning in dangerous environments of rescue with machine-to-machine or human-machine collaboration. Supported by cloud environment, Disaster Management Support Systems provide different real-time data analysis services. In areas of the world that suffer from severe weather driven-hazards such as storm surges, flooding, or heavy snow and wind, a greater effort mediated by cooperatives and National Disaster Management Agencies is needed. There is an opportunity to develop Cloud Systems that bring together hydrological, meteorological, and impact models at high spatial resolution for forecast and early warning in these regions. Such a service will be essential not only for local, national, and regional receivers of the information but also for adjacent areas that may be indirectly affected by the potential trans-boundary effects of severe weather events.
Operational Transparency as a Compliance Mechanism in Federal DevOps Ecosystems
Transparency in operations is a valuable aspect of establishing trust and compliance in operational federally-managed DevOps ecosystems. This paper focuses on the use of automation driven by transparency, particularly automated observability, standardized reporting, as well as immutable records of change as an effective tool of compliance. These are mechanisms that contribute towards enhancing audit readiness and accountability by the stakeholders in a bid to create awareness of what transpires in the system. The automated observability has a benefit of real-time monitoring compared to the standardized reporting which offers uniformity in recording of processes during audit making it more efficient. The records of unalterable change are also conducive to integrity of the operations in the system with any changes in the system always being recorded and can be traced. The synthesis of all these practices into federal DevOps space produces the atmosphere of compliance governance, which enables to provide the software in a safe manner and avoid potential risks. The paper notes that transparency is necessary to reduce the instances of non-compliance and facilitate continuous monitoring that results into the realization of long-term success in government IT systems.
Multi-Cloud Strategy and Governance Models for Enterprise IT Management
This paper examines multi-cloud strategy and governance models for enterprise IT management, focusing on how organizations can leverage multiple cloud service providers to achieve flexibility, resilience, cost efficiency, and regulatory compliance; it proposes an integrated governance framework that aligns cloud strategy with business objectives through standardized policies for security, data management, vendor control, performance monitoring, and cost optimization, enabling enterprises to manage complexity, mitigate risks, and maximize value from heterogeneous cloud environments
Implementing Role-Based Access Control for Healthcare Data using SharePoint
The increased need to have secure and efficient data management in the healthcare has resulted in the use of Role-Based Access Control (RBAC) systems that control access of sensitive information according to the role of the user. The given research is devoted to the implementation of the RBAC system in healthcare data management on the basis of the SharePoint platform that is highly popular in document management and collaboration. The paper details the creation and creation of a powerful access control system, which suits healthcare institutions, considering that it complies with strict privacy laws, including HIPAA. These frameworks involve user authentication, role assigning and data access controls that are incorporated into the SharePoint environment to establish access controls that will be based on defined user roles. The paper goes on to address the issues and best practices when implementing this system including the definition of roles and permissions, dynamic user groups and system scalability. The case study is offered to illustrate how the framework has been effective in a healthcare environment, and how it enhances data safety, efficiency, and compliance with regulations. The study provides the audience with the prospects of SharePoint as an elastic and scalable software to handle healthcare information and provide a secure access control system. The next step in work will be the optimization of the framework to cover the new challenges of healthcare data management
Designing End-to-End Retrieval-Augmented Generation(RAG) Workflows for Knowledge-Intensive Applications
Creating effective workflow processes on Knowledge-Intensive Applications (KIAs) is now critical towards the utilization of the full potential of retrieval-augmented generation (RAG) models. The study provides a detailed model of the end-to-end RAG processes development and use and aims to optimize the communication between the retrieval and the generation processes to improve the efficiency of the knowledge integration during the complex tasks. The suggested framework focuses on modularity and scalability which allows it to be easily integrated with different sources of knowledge, including document corpora and databases. The workflow will be developed to support the efficient retrieval of information with the help of advanced indexing and ranking methods and then generate high-quality responses using the pre-trained language models. An important feature of the design is the feedback loop between retrieval and generation that is iterative in nature and which makes the model adapt and improve with time. Other issues and prospects of life cycle RAG systems in different applications examined in this paper include the automated customer support, decision-making tools, and research assistants. The comparison of the framework shows that there are overwhelming improvements in accuracy of tasks, relevancy of responses, and the overall performance of the system in comparison to the traditional models. The findings offer practical findings to be considered in the future development of knowledge-oriented AI programs, where it is important to foster knowledge retrieval and content generation.
Optimizing User Experience in High-Traffic Financial Web Applications Using Analytics
Financial web systems that operate on a high volume have tight performance, reliability, and regulatory limits and cater to large and diverse user base. To achieve optimal user experience (UX) in these settings, user experience refinement is not enough, but rather data and analytics-informed architectural choices. In this article, the authors analyze analytics-based UX optimization in large-scale financial web applications, where real-time behavioral data is used to discover the points of friction, decrease abandonment, and enhance the efficiency of the systems. The paper discusses the implementation of event-based analytics into frontend processes to log interactions by the users, create behavior, and drop-off trends at scale. Such insights are converted into specific UX and performance enhancements, such as simplifying work flows, providing better validation feedback, and enhancing interaction flows. Implementations at the production level show significant improvements in submission accuracies, submission completion, and customer satisfaction and decreases operational loads in the support systems. The results indicate that analytics-based UX optimization is an important architectural feature to develop robust, user-centric financial systems in enterprise environments with high traffic.
Cloud Native Microservices and IoT Architectures for AI Driven Fraud Detection Predictive Maintenance and Mobile Healthcare Intelligence with SAP
The rapid evolution of cloud-native technologies, Internet of Things (IoT), and artificial intelligence (AI) has transformed the design of intelligent enterprise systems. This paper explores how cloud-native microservices architectures integrated with IoT platforms enable scalable, resilient, and real-time AI-driven applications for fraud detection, predictive maintenance, and mobile healthcare intelligence within SAP ecosystems. Traditional monolithic enterprise systems struggle to handle the volume, velocity, and variety of data generated by connected devices and digital transactions. Cloud-native microservices, deployed on container orchestration platforms, provide modularity, elasticity, and fault tolerance, making them suitable for data-intensive AI workloads. IoT architectures facilitate continuous data ingestion from sensors, medical devices, and transactional systems, while AI models perform real-time analytics, anomaly detection, and predictive insights. SAP technologies such as SAP Business Technology Platform (BTP), SAP IoT, SAP HANA, and SAP AI Core enable seamless integration of business processes with advanced analytics and machine learning. This study examines architectural patterns, deployment strategies, and data pipelines supporting AI-driven intelligence across multiple domains. The paper also discusses research challenges, benefits, and limitations associated with security, latency, data governance, and system complexity. The findings highlight the importance of cloud-native and IoT convergence in building intelligent, scalable, and sustainable enterprise AI solutions.
A Secure AI and Cloud Based Architecture for Public Sector Biomedical and Financial Systems Enabling Mobile Healthcare Communication and Equitable Access
Public sector digital transformation increasingly depends on the convergence of biomedical healthcare systems, financial platforms, and cloud-native infrastructures to deliver inclusive and secure services. Mobile healthcare communication, digital payments, and public health financing introduce significant challenges related to data privacy, interoperability, resilience, and equitable access. This paper proposes a secure AI-driven cloud architecture that integrates public sector biomedical systems with financial platforms to enable real-time mobile healthcare communication while ensuring governance compliance and technological equity. The proposed architecture leverages artificial intelligence for decision intelligence, anomaly detection, and fraud prevention, alongside cloud-native security controls such as zero-trust access, encryption, and policy-driven governance. By incorporating equity-aware design principles and resilience mechanisms, the framework supports scalable and inclusive service delivery across diverse socio-economic environments. The study demonstrates how secure integration of financial and healthcare systems can improve service reliability, trust, and accessibility in public sector digital ecosystems
Cloud-Native Real-Time Analytics with Embedded Cyber Defense Using AI and Secure APIs
In the era of exponential data growth, organizations increasingly rely on cloud-native solutions to process and analyze real-time data efficiently. Integrating artificial intelligence (AI) with secure application programming interfaces (APIs) enables organizations to not only gain actionable insights but also embed robust cyber defense mechanisms within their analytics pipelines. This study explores the design and implementation of cloud-native architectures that facilitate real-time analytics while simultaneously ensuring cybersecurity. By leveraging AI-driven threat detection, anomaly detection, and predictive analytics, cloud platforms can autonomously identify and mitigate potential security threats in real time. Secure APIs serve as the backbone of these systems, providing encrypted communication channels, authentication, and authorization mechanisms to prevent unauthorized access. The paper highlights current methodologies, implementation strategies, and challenges associated with integrating AI-based cyber defense into cloud-native analytics platforms. Additionally, it examines the advantages of scalability, cost-effectiveness, and rapid deployment alongside potential limitations, such as complexity and dependency on cloud providers. The research provides a holistic perspective on combining cloud computing, AI, and secure APIs to enhance both analytical capabilities and cybersecurity resilience. Practical recommendations and frameworks are proposed to guide future development in secure, intelligent, cloud-based analytics systems.
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
Automation, Compliance, And Public Health Reliability In Biomedical Infrastructure
Automation within biomedical computing environments has emerged as a critical determinant of public health system reliability, fundamentally transforming how healthcare organizations maintain data integrity, security, and operational consistency. The proposed framework, informed by publicly available compliance standards from the National Institutes of Health and National Library of Medicine, demonstrates how an integrated approach to incorporate the security validation and enforcing policies directly into the DevOps pipelines achieves significant minimization of human error and speeding up the delivery of research comes at a significant price. Configuration management tools are fundamental elements in providing consistency and repeatability in distributed computing environments by ensuring desired infrastructure states and eliminating configuration drift. Continuity based on security-centered static analysis tools integrated into continuous integration environments can be effective for vulnerability detection, provided that the choice of tools and the effective integration of workflows are considered. The framework demonstrated measurable outcomes including a significant decrease in the number of configuration deviation incidents, a reduction in the approval cycle time, and a substantial decrease in the number of mistakes in compliance documentation. In addition to operational measurements, automated compliance systems enable fair access to systems, prevent the misuse of sensitive patient information through defense-in-depth security designs, and address the challenges of computational reproducibility related to scientific integrity. This framework uniquely positions automation as a public-trust mechanism, where systems handling health data operate with consistent accountability and transparency. This transformative paradigm converts regulatory compliance from an administrative burden into a strategic enabler of research excellence, providing a replicable architectural model for public health institutions seeking to balance security imperatives, operational efficiency, and innovation velocity in support of improved healthcare outcomes.
Frameworks for Ensuring Compliance in Digital Platform Governance
The rapid increase in the digital platforms has altered the world economies and introduced innovation, connectivity and economic development. However this growth has also raised advanced governance problems particularly in regards to ensuring that legal and ethical standards are followed. The article is a study on the structures that should be in existence to ensure compliance in the digital platform governance. It examines the existing frameworks and proposes a common manner of establishing a balanced regulatory condition which will be founded on regulatory control, transparency procedures and stakeholder participation. This paper analyses how legal frameworks, technology infrastructure, and organizational policies can interact to promote compliance and inhibit risks related to privacy of information, bias in algorithms, market monopoly and consumer protection. The other factors of compliance that are emphasized in the study and are critical to promoting trust and equity in the digital mediums are auditability, accountability, and user empowerment. In addition, the paper will discuss how the existing technologies can be leveraged to automate the compliance processes, in order to offer real-time monitoring and resolving the issues i.e. artificial intelligence and machine learning. It also defines regulatory fragmentation, jurisdiction and dynamic nature of digital technologies as a matter of concern and provides recommendations on how to create dynamic and flexible structures. These frameworks are very important in balancing between innovation and responsible governance. The article is targeted at educating the policymakers, platform operators, and users concerning how the collaboration and continuous adaptation would enhance the efficiency of the digital platform governance and compliance.
A Secure and Ethical AI Cloud Framework for SAP-Centric Enterprise Automation Integrating Mobile Broadband Networks and Dynamic Data Warehousing
The rapid convergence of artificial intelligence, cloud computing, mobile platforms, and broadband networks has transformed modern enterprises, yet it has also introduced significant challenges related to security, ethics, scalability, and regulatory compliance. This paper proposes a unified AI-driven and cloud-native enterprise architecture that integrates ethical automation, secure mobile platforms, high-performance broadband networks, and compliance-aware decision intelligence into a cohesive framework. The proposed architecture leverages cloud-native principles such as microservices, containerization, and orchestration, combined with AI-based analytics and decision intelligence to enable adaptive, scalable, and trustworthy enterprise systems. Ethical automation is embedded through transparent AI models, governance mechanisms, and accountability controls, while secure mobile platforms are supported via zero-trust security models and end-to-end encryption. Broadband networks act as a foundational enabler, ensuring low-latency, high-availability connectivity essential for real-time AI inference and mobile access. Compliance-aware decision intelligence integrates regulatory constraints directly into AI-driven decision processes, reducing organizational risk and improving trust. This research synthesizes existing literature, proposes a methodological framework, and discusses advantages, limitations, and empirical implications. The study concludes that a unified approach is essential for sustainable digital transformation in highly regulated and data-intensive enterprise environments.
Resilience Engineering for Intelligent Enterprise Platforms
With the change of enterprise platforms to add more automation and intelligence, the systems experience new operational risks, including decision instability, data drift, and more system breakdowns. This paper discusses how resilience engineering concepts can be applied to intelligent enterprise systems, and a model is introduced that can be used to improve system reliability in the context of increasing complexity. The architectural strategies suggested focus on the fault containment, graceful degradation, and recoverability. The main attributes of the framework are decoupling of learning and optimization elements with implementation paths and the addition of safety nets such as limited autonomy, back-up logic and controlled shutdown. The way that intelligence is conceptualized as an ability that is controlled and not a necessity helps to guarantee the platform stability and reliability even during the time of uncertainty and operational pressure. The article will be applicable in developing enterprise platforms capable of supporting performance and reliability as automation and complexity keeps on growing as it gives a blueprint on the resilient enterprise architecture of tomorrow
Generative AI in enterprise systems: Moving beyond conversational AI
Generative Artificial Intelligence has evolved beyond conventional applications into a transformative enterprise technology that fundamentally reshapes organizational capabilities across multiple domains. This article explores how Gen AI transcends simplistic chatbot implementations to deliver substantive value through enterprise process automation, intelligent software development, and advanced predictive analytics. Integrating Gen AI into enterprise ecosystems creates profound operational efficiencies by optimizing development lifecycles, automating complex workflows, and enabling dynamic business intelligence. When embedded effectively, these technologies facilitate real- time adaptation to market conditions through predictive insights and automated response mechanisms. The article examines how forward-thinking organizations leverage Gen AI for competitive advantage through accelerated software development, enhanced decision-making capabilities, and proactive operational adjustments. Additionally, the discussion addresses critical implementation challenges, including data quality considerations, regulatory compliance requirements, and ethical imperatives surrounding algorithmic bias and governance frameworks. The comprehensive evaluation demonstrates that Gen AI represents an incremental technological advancement and a fundamental paradigm shift in how enterprises conceptualize and implement intelligent systems to navigate increasingly complex business environments.
Architecting Scalable API-Led Integrations Between CRM and ERP Platforms in Financial Enterprises
Integration of Customer Relations Management (CRM) and Enterprise Resource Planning (ERP) platforms in financial institutions has developed from a technical requirement to a strategic imperative. This article examines how AP-LED connectivity converts traditional point-to-point integration into a flexible, scalable architecture that drives significant commercial value. Through comprehensive data from financial enterprises, a threelayer API architecture- the impact of implementing the system API, procedure API, and experience API- makes a sufficient improvement in operational efficiency, compliance management, and customer experience. The change in the modular, reinforcing integration assets from unbroken architecture addresses the unique challenges of financial services, including regulatory compliance, data security, and real-time transaction processing. Technical design ideas such as error handling, versioning, data transformation, safety compliance, and performance adaptation suggest how financial institutions can effectively apply these architectures. An elaborate case study of an order-to-cash process shows the practical application of these principles, providing concrete evidence of business results obtained through AP-LED integration between CRM and ERP platforms. As financial services become digital, well-designed API integration represents a commercial imperative to maintain competitive advantage in the rapidly developing market
Designing Carrier-Grade Microservices for Telecom: Ensuring Availability and Scale in Order Fulfillment Systems
One of the issues telecom providers have to tackle is the ability to provide scalable services with zero downtimes to enable real time transactions with customers. The current paper describes an architecture of microservices that can be used in the carrier-grade telecommunications environment, focusing on resilience and high availability of order fulfillment systems. This framework proposed makes use of Spring Boot, Apache Kafka, and Kubernetes with the guarantee of transaction as well as the protection against fault on the web and agent channels promises elastic scalability and fault tolerance. The system was tested with load tests and recovery simulations and the resultant confirmation specifying better MTTR and availability of more than 99.997%. Decoupling and self-healing orchestration based on Kafka contributed highly to robustness of the system. Such results serve as a guide to telecom operators who want to transform their monoliths into fault- tolerant microservice environment.
Engineering Audit-Ready CI/CD Pipelines for Federally Regulated Scientific Computing
When the federal regulator controls the scientific computing platforms they must strike a balance between speedy delivery of software and high-quality audit requirements that mandate the presence of evidence that can be replicated, controlled change, and evidence of tampering records. The paper proposes an engineering strategy of constructing audit ready CI/CD pipelines that generate provisable provenance, traceable change histories and cryptographically verifiable artifacts as an objective and not an ex post facto account of such. The managed DevSecOps controls of the strategy are provided across the delivery lifecycle, including policy-as-code compliance gates, automated security and quality assurance, immutable audit trail production, and secure software supply chain to sign and verify artifacts. The research also illustrates the capability of pipeline instrumentation to offer an end to end lineage between the source commit, through build and test and package and deployment at the same time without violating separation of duties and approval traceability as is required in the IT environment of the public sector based on regulated biomedical scientific computing systems as reference implementations. Evidence is produced continuously in a machine-verifiable format, which is structured, and on which auditors and other authorizing figures can re-assemble what and why was changed, who had authorization, what controls were executed, and what exactly was placed in each environment. Comparison of release cycles indicates that the standardized evidence package leads to an increase in audit preparedness, the reduction of the hectic authorization schedules by the avoidance of rework and avoidance of records omissions, and transparency of release governance and software assurance stakeholders. The findings show that making auditability a first-class engineering requirement can be used to improve the integrity and reproducibility without totally degrading the delivery cadence. The methodology has been extended to investigate computing infrastructure communities which must match federal specifications such as NIST control families and FISMA-based regulation and provides a plausible path forward to functioning towards operationalizing compliant continuous delivery inside regulated scientific computing setting.
A Holistic AI and Cloud Governance Model for Demand Forecasting and Business Optimization with Risk Aware Cybersecurity
The rapid adoption of artificial intelligence (AI) and cloud computing has transformed enterprise operations, enabling real-time demand forecasting, business optimization, and advanced cyber risk management. However, organizations often face challenges in aligning AI-driven decision-making with cloud governance policies, compliance frameworks, and security protocols. This research proposes a holistic governance model integrating AI and cloud computing to optimize business processes while mitigating cyber risks. The model emphasizes the orchestration of predictive analytics for demand forecasting, AI-driven resource optimization, and robust security frameworks for proactive cyber risk management. By leveraging scalable cloud infrastructure, enterprises can ensure high availability, elasticity, and secure handling of sensitive data. The study combines theoretical frameworks, industry best practices, and case-based insights to validate the model’s effectiveness. Findings indicate that a unified AI-cloud governance framework enhances operational efficiency, reduces forecast errors, and strengthens organizational resilience against cyber threats. This research contributes to bridging the gap between emerging AI capabilities and cloud governance mechanisms, providing a structured approach for enterprises to achieve both operational excellence and cyber risk mitigation.
A Playbook For Enterprise Application Modernization Using Microservices And Headless CMS
ABSTRACT: Updating the old enterprise software is a fundamental need in the development of organizations seeking to increase their scalability, flexibility and user experience in the fast-changing digital world. In this paper, they introduce a playbook of how to go about moving away towards monolithic systems to organizations where the systems are microservices based and built in with their headless CMS. Based on an empirical case study and empirical assessment, the playbook identifies a strategic approach to the method of decomposition, alignment of CI/CD pipelines, performance, and the division of the frontend and backend by using headless CMS. Analysis by industries shows that there has been a great improvement in the frequency of deployment, response time, fault tolerance, and development agility provided through the deployment of multiple applications of quantitative testing and qualitative testing. Furthermore, the combination of headless CMS and serverless functions is an effective enabler to deliver omnichannel content since it can shorten the release time and the resource burden. The results combine the best architectural practices, organizational change knowledge, and technology stacks recommendations. The present work adds to the body of knowledge by presenting a scalable and practical roadmap that can be used both as a technical and cultural transformation roadmap on modernization, which can be used as a reference to the team members in enterprise architecture, DevOps, and digital transformation leaders.
Automation and DevOps in Database Management: Advancing Efficiency, Reliability, and Innovation in Modern Data Ecosystems
The present paper addresses the advantages of automation and DevOps as a hybrid to maintain databases within the existing systems. It shows how such tools reduce the number of manual operations, increase reliability of the work, and decrease the period of data work. The use of Artificial Intelligence and Machine Learning in enhancing automation by means of smart tuning and testing is also discussed in the paper. The results indicate that DevOps assists the teams to cooperate better and implement safe and rapid updates. There are also better security and compliance with the help of automation. This paper demonstrates that databases become more efficient, stable, and prepared to face challenges of cloud and data problems in the future through automation and DevOps.
Scalable AI-Driven Cyber-Physical Systems for Secure Cloud and 5G Networks: Predictive Analytics, Reliability, and Sustainable Energy Integration
The convergence of cloud computing, 5G networks, and cyber-physical systems (CPS) has enabled highly connected, data-intensive enterprise and critical infrastructure environments. However, this convergence also introduces challenges related to scalability, security, reliability, and energy sustainability. This paper proposes a scalable AI-driven CPS framework for secure cloud and 5G networks that integrates predictive analytics, reliability engineering, and sustainable energy management. The framework leverages machine learning and generative AI models to enable real-time monitoring, anomaly detection, predictive maintenance, and autonomous decision-making across distributed physical assets and virtual network functions. Security is embedded through zero-trust principles, AI-assisted threat intelligence, and policy-aware orchestration, while reliability is enhanced using adaptive fault prediction, redundancy optimization, and self-healing mechanisms. In parallel, energy-aware AI models optimize power consumption across cloud data centers, 5G base stations, and edge nodes by integrating renewable and sustainable energy sources. The proposed architecture supports mission-critical applications such as smart cities, industrial automation, intelligent transportation, and healthcare systems. By unifying AI-driven analytics with CPS, cloud-native architectures, and 5G networking, this work demonstrates a holistic approach to building resilient, secure, and energy-efficient digital infrastructures capable of meeting future scalability and sustainability requirements.
Building Secure Biometric Systems for Digital Identity Verification in Aviation Mobile Apps
With mobile platforms becoming a part of the aviation business, it is becoming increasingly important to be able to guarantee the security, efficiency, and compliance with privacy requirements of passenger identity verification. The study introduces a new solution to the concept of embedding secure biometric solutions in mobile platforms to verify the digital identity of people during aviation. The offered system utilizes the facial recognition technology, the use of OAuth 2.0-based authentication, permission-based biometric processes, and makes possible contactless passenger confirmation in the mobile check-in and boarding. The paper explains how the system was designed including the protection of privacy, fall back identity as well as the resilience of the system when the number of users using it at once is high. In addition, it deals with session management of secure mobile authentication, which allows adherence to aviation security standards. The findings of the deployment show that the biometric solution can achieve a significant decrease in the check-in time, the better passenger experience, and adherence to the data protection regulations. As the paper illustrates, mobile biometric identity solutions can be a very reliable and more scalable method of enhancing air travel infrastructure, provided with effective privacy controls and security measures, which are in line with the current demands of efficiency and security in digital identity management
Adaptive Cryptographic Orchestration Against Learning-Enabled Adversaries
Artificial intelligence (AI) has transformed the cyber threat landscape by enabling adversaries to automate reconnaissance, optimize probing strategies, and adapt attacks using feedback. While modern cryptographic primitives remain mathematically secure under standard assumptions, real-world cryptographic deployments often expose operational signals—such as timing behavior, request patterns, error responses, and negotiation outcomes—that learning-enabled adversaries can exploit to improve attack efficiency. This paper addresses the systems-level problem of predictable cryptographic deployments enabling adversarial learning. We propose Adaptive Cryptographic Orchestration (ACO), a closed-loop defensive framework that dynamically reconfigures cryptographic and protocol parameters in response to telemetry-driven threat estimation, while preserving the use of standardized and vetted cryptographic primitives. We formalize attacker–defender interaction as a non-stationary learning environment, demonstrate why adaptive reconfiguration disrupts reinforcement-learning convergence, and present a simulation-based evaluation showing that ACO significantly reduces attacker learning efficiency under bounded operational overhead. ACO complements traditional cryptographic guarantees by reducing operational predictability in AI-accelerated attack settings.
Optimizing Data Flow: A Unified Approach for Product, Pricing, and Revenue Sync in Enterprise Systems
This study proposes an integrated strategy towards maximizing the flow of data within enterprise systems by harmonizing product, pricing and revenue synchronization. The suggested model takes advantage of MuleSoft-based integration to simplify the process of data interchange between ERP and CRM and financial platforms. The architecture standardises the communication of key data aspect by embracing canonical data models and transformation orchestration, which guarantees a smooth communication among disparate systems. Other features that are included in the framework are centralized governance frameworks and error management, which strengthens the integrity of data, financial integrity, and transparency of operations. With a single data synchronization system, silos are removed, less manual intervention is made, and fewer errors are minimized, all of which lead to more efficient and accurate decision-making. Moreover, MuleSoft APIs integration promotes scalability and flexibility, and organizations can easily incorporate the changes in the business needs. The research shows that the proposed architecture does not only help to streamline the data flow between different systems of an enterprise, but also to promote the performance of the whole organization in terms of improved product, pricing, and revenue management. The study offers a worthy paradigm to businesses that want to maximize their data synchronization activities to achieve real-time, correct information and enhanced functionality throughout all their networks.
Secure Closed-Loop AI Decision Intelligence for Enterprise Transportation and Regulatory Systems with EV Infrastructure
Closed-loop and causal artificial intelligence (AI) systems have emerged as transformative technologies for supporting real-time decision intelligence across complex enterprise transportation networks and regulatory environments. Traditional AI approaches primarily rely on correlation and batch learning, often failing to adapt swiftly to dynamic conditions or provide robust explanations for decisions, which is critically important in safety-critical and compliance-regulated industries. Closed-loop AI refers to systems that continuously monitor outcomes, update models, and adjust actions autonomously, thereby enabling self-correcting behavior. Causal AI enhances interpretability and decision quality by modeling causal structures rather than mere statistical associations. This paper explores how integrating closed-loop learning with causal reasoning generates decision intelligence capabilities that drive adaptive operational performance in enterprise transportation systems, ranging from logistics and fleet management to real-time regulatory compliance monitoring. Through an extensive literature review, we situate current research developments and gaps. We propose a research methodology including data sources, simulation environments, and evaluation metrics tailored to causal closed-loop systems. Our results indicate significant improvements in decision accuracy, responsiveness to regulatory changes, and operational resilience. We conclude by outlining future research trajectories aimed at wider adoption of these AI paradigms in transportation and regulatory ecosystems.
Generative AI–Enabled Decision Intelligence: An Integrated Analytics and Autonomous Systems Framework for Cybersecurity and Retail Enterprises
Enterprises operating in highly dynamic digital ecosystems face increasing pressure to make timely, accurate, and secure decisions across retail operations, cybersecurity management, and strategic governance. Traditional analytics systems, which rely primarily on historical and structured data, are no longer sufficient to address complex, context-aware decision requirements. This study proposes an integrated generative AI and analytics framework designed to enhance enterprise decision intelligence while simultaneously strengthening cybersecurity resilience and retail optimization capabilities. The framework unifies predictive analytics, generative AI models, and security-aware data pipelines to enable context-driven forecasting, anomaly detection, and automated decision support. By incorporating multivariate data sources such as consumer behavior, operational signals, threat intelligence feeds, and external contextual factors, the proposed approach enables enterprises to move from reactive decision-making to proactive and adaptive intelligence. The framework also emphasizes governance, fairness, and explainability to ensure trust and compliance in AI-driven systems. Through architectural analysis and methodological design, this research demonstrates how generative AI can augment enterprise analytics platforms to deliver scalable, secure, and actionable insights. The proposed model provides a unified foundation for retail demand optimization, enterprise operational intelligence, and cybersecurity risk mitigation, contributing to the development of next-generation intelligent enterprise systems.
Designing an Advanced Quality Assurance Framework to Ensure Accuracy, Regulatory Compliance, and Operational Reliability across End-to-End Mortgage Origination and Underwriting Platforms
Mortgage origination and underwriting platforms operate within highly regulated, data intensive environments where accuracy, compliance assurance, and operational reliability are critical to institutional credibility and financial stability. This study addresses the persistent limitations of conventional quality assurance practices that remain fragmented, reactive, and insufficiently aligned with the end to end mortgage lifecycle. The primary objective is to design and evaluate an advanced quality assurance framework capable of systematically integrating data integrity controls, regulatory rule consistency, workflow reliability, and audit traceability across mortgage technology platforms. The research adopts a mixed methodological approach that combines conceptual modeling, analytical evaluation, and scenario based assessment grounded in documented industry practices and peer reviewed system behavior studies. The findings demonstrate that integrated quality assurance architectures significantly reduce defect propagation, enhance regulatory stability, and improve operational resilience when compared with traditional testing centric models. The proposed framework introduces a structured layering of quality controls that enables continuous monitoring, proactive risk identification, and transparent compliance validation. From an academic perspective, the study contributes a domain specific synthesis that bridges software quality engineering and financial systems governance. From an industry standpoint, the framework offers a practical reference model for organizations seeking to strengthen quality governance while supporting scalability and regulatory adaptability. The study concludes that quality assurance must be repositioned as a strategic architectural capability rather than an isolated control function, with implications for future research on intelligent automation, governance integration, and resilient financial system design.
operational reliability, mortgage lifecycle management
compliance risk mitigationscalable QA models
Strategic Implementation of NoSQL Technologies in Modern Enterprise Data Architectures
Enterprise Data Management is undergoing a paradigm shift from relational database systems to NoSQL technologies. The present evaluation provides a systematic assessment of the NoSQL adoption journey undertaken by enterprises, with a specific focus on the architectural benefits of NoSQL, their performance characteristics, and their use cases in enterprise environments. This article confirms that performance enhancement is feasible with NoSQL systems based on the range of data models they implement, including key/value systems, document stores, column-family systems, and graph databases. Each data model addresses enterprise requirements while leveraging an architecture with horizontal scalability, flexible schema, and a data access pattern that is not efficient with relational systems. Each NoSQL database implementation contains architectural advantages that can provide enterprises with improved performance for targeted workloads, deploy microservices architecture in enterprise environments, and implement analytics with real-time capabilities. Although there are operational complexities involved with hybrid architectures as well as integration challenges associated with NoSQL technologies that enterprises must navigate when adopting NoSQL databases, the results show how the challenges associated with data consistency models, streaming architectures, and hybrid persistence strategies differ when utilizing both traditional and NoSQL systems with an operational downside.
Re-Architecting Enterprise Integration Platforms: From SOA and ESB to API-Led Connectivity
Enterprise integration architectures have undergone a significant transformation over the past two decades, evolving from tightly coupled Service-Oriented Architecture (SOA) and Enterprise Service Bus (ESB)–centric models toward more flexible, API-led connectivity paradigms. While SOA and ESB platforms successfully enabled standardized service reuse and internal system interoperability, they often struggled to meet modern enterprise demands for agility, scalability, cloud adoption, and rapid digital innovation. The emergence of API-led architectures, microservices, and cloud-native integration platforms has reshaped how enterprises expose, consume, and govern integration capabilities across heterogeneous environments. This article examines the architectural evolution of enterprise integration platforms, focusing on the transition from traditional SOA and ESB-based integration models to modern API-led connectivity approaches. It analyzes the limitations of legacy integration patterns, the architectural principles underpinning API-led connectivity, and the role of hybrid integration platforms in enabling coexistence during transformation. Key architectural layers, integration patterns, governance considerations, security models, and operational impacts are discussed in detail. The article also explores migration strategies that allow enterprises to modernize incrementally without disrupting mission-critical systems. Through conceptual diagrams, comparative tables, and architectural analysis, this work provides a comprehensive and practical framework for re-architecting enterprise integration platforms to support digital transformation, cloud readiness, and long-term scalability.