International Journal of Computer Technology and Electronics Communication
Securing Mobile App Development with Compliance Aware CI/CD Pipelines in Government
Government mobile applications should undergo a high level of regulatory controls, security baseline and release governance that is often not readily implemented in a traditional CI/CD pipeline. The given research suggests a compliance-conscious CI/CD design that is specifically designed to address mobile apps that are deployed in government and other settings of public sectors, where traceability, controlled releases, and verifiable security are obligatory. The proposed architecture integrates secure code signing with environment specific configuration management with provisioning automation to avoid misconfiguration in development, staging and production. It has integrated constant security habits by using automated statical and dependency exploration, secret recognition, and artifact integrity assurance and generates evidence that can be ingested in investigations without reducing the speed of conveyance. A release gating layer that is based on policies implements governance rules like mandatory approvals, segregation of responsibilities and risk-based promotion policies before builds are allowed to move to channels of distribution. The framework also enforces the auditable deployment of both iOS and Android, builds provenance, signing events, scan reports, and release decisions are stored in tamper-evident logs. In the real world, it can be seen that it can lead to visible improvements: a reduction in release cycles, the number of deployment failures, and the ability to comply with compliance requirements at times of high demand on the service to the population (e.g. periods of enrollment). The framework allows government teams to scale mobile delivery at the same time by aligning DevOps automation with regulatory controls to ensure security assurance and accountability of release. The paper concludes by having useful recommendations of how the adoption can be introduced such as the suggested controls, patterns of pipelines and operational concerns that can be applied to maintain compliance in the long term
Human-Centered Experience Engineering through Cognitive Design Patterns in Web-Based Systems
Human-centered experience engineering has become a critical discipline in the design of complex web-based systems as interaction density, functional scope, and user expectations continue to grow. Traditional user interface design approaches often emphasize visual aesthetics or isolated usability improvements, while overlooking the cognitive processes that shape how users perceive, reason, and act during interaction. This paper proposes a structured framework for human-centered experience engineering grounded in cognitive design patterns that systematically align system behavior with human cognitive capabilities. The study examines foundational cognitive principles such as attention management, working memory limitations, mental model formation, decision-making effort, and error perception, and demonstrates how these principles can be translated into reusable design patterns for web systems. A taxonomy of cognitive design patterns is introduced and mapped to experience intents, followed by an architectural perspective that explains how these patterns are operationalized within component-based front-end systems. The paper further presents evaluation methodologies for assessing cognitive experience quality and validates the proposed approach through an enterprise web workflow case study. Governance and operationalization strategies are discussed to ensure consistency and scalability across teams and systems. The findings illustrate that embedding cognitive design patterns as architectural constructs leads to improved interaction clarity, reduced cognitive load, and increased user confidence, establishing a sustainable foundation for human-centered web experience engineering
Enterprise Architecture for Real-Time Intelligence in Distributed Environments
In the digital transformation, the contemporary businesses are becoming more dependent on real time intelligence to function well in the distributed, latency sensitive environments. This article introduces a novel enterprise architecture that smoothly incorporates single centralized cloud systems and distributed edge and on-premise systems. The architecture is made so as to maximize real-time decision-making by enabling organizations to adapt with flexibility to dynamic factors and at the same time remain scalable and responsive. The suggested design will consider a number of enterprise limitations, including network variability, data governance boundaries, cost optimization, and operational challenges of operating such systems. The main characteristics of the architecture are the strategic placement of workloads, life cycle coordination of models and rules deployed, and a high level of observability linking the effects of the distributed systems to centralised governance structures. This is a handy reference architecture that offers a systemic methodology to real-time intelligence to businesses on how to fulfill control, compliance, and sustainability of the system in the long run. The strategy is especially useful in cases when organizations aim at using the potential of distributed systems but do not want to sacrifice system integrity or efficiency.
BRIDGING DESIGN AND DEVELOPMENT: BUILDING A GENERATIVE AI PLATFORM FOR AUTOMATED CODE GENERATION
The disconnect between design and development phases in software engineering often leads to increased development cycles, misinterpretations, and reduced productivity. Recent advancements in Generative AI, particularly large language models (LLMs), offer promising capabilities for automating code generation directly from high-level design artifacts or natural language prompts. This paper presents the architecture and implementation of a generative AI-powered platform designed to bridge the gap between UI/UX design and functional code development. The platform integrates components such as prompt engineering layers, pre-trained LLMs, design parsers, and code validators to convert design inputs into production-ready code. We evaluate the system using two real-world use cases: automatic transformation of web form designs into ReactJS code and full-stack application scaffolding from Figma prototypes. Our experimental results demonstrate significant reductions in development time and manual effort, with an average code generation accuracy exceeding 85%. Additionally, the platform enhances collaboration between designers and developers by streamlining the transition from mockups to executable components. The findings highlight the potential of generative AI in accelerating software delivery, reducing human error, and enabling rapid prototyping in modern development environments. Future enhancements include support for multi-modal inputs, continuous learning, and integration with CI/CD pipelines
AI-DRIVEN COMPLIANCE: USING DATA SCIENCE TO ENSURE FAIR PRICING AND POLICY ALIGNMENT IN HEALTHCARE SYSTEMS
The rapid expansion of artificial intelligence (AI) and data science in healthcare has introduced new opportunities for achieving regulatory transparency, fairness, and ethical pricing. However, the complexity of healthcare economics—combined with evolving policy frameworks—poses significant challenges to ensuring compliance and equitable cost distribution. This paper presents an AI-driven compliance framework that leverages advanced analytics, A/B testing, Principal Component Analysis (PCA), and Receiver Operating Characteristic (ROC) analysis to detect bias, assess model validity, and optimize pricing fairness within healthcare systems. By integrating machine learning algorithms with statistical validation, the proposed framework identifies anomalies in healthcare pricing, quantifies fairness levels, and evaluates policy adherence across diverse patient demographics and provider types.Experimental results based on simulated healthcare cost datasets demonstrate that AIbased compliance models outperform traditional manual audits by increasing pricing transparency, reducing bias variance, and improving detection accuracy of noncompliant pricing behaviors. The findings suggest that data-driven compliance mechanisms can significantly enhance ethical governance, regulatory alignment, and trust within healthcare ecosystems
AI-Powered Cloud Orchestration: Automating Multi- Cloud & Hybrid Cloud Workloads
AI-powered cloud orchestration revolutionizes how enterprises manage and optimize their multi-cloud and hybrid cloud environments. Integrating artificial intelligence into cloud management addresses complexity, manual intervention, and reactive problem-solving challenges that plague traditional orchestration methods. By implementing intelligent algorithms for resource allocation, workload balancing, predictive scaling, security enhancement, and self-healing capabilities, organizations can transform their cloud operations from manually-defined workflows to autonomous systems capable of continuous optimization. These advanced orchestration technologies enable dynamic resource distribution based on usage patterns and forecasted demand while simultaneously identifying cost-saving opportunities through workload consolidation and intelligent scheduling. Security frameworks are significantly strengthened through anomaly detection, predictive threat intelligence, and adaptive access control policies that evolve with changing organizational needs. Perhaps most transformative is the ability of self- healing infrastructure to automatically detect, diagnose, and remediate issues before they cause service disruptions, dramatically reducing the operational burden on technical teams and allowing them to focus on innovation rather than troubleshooting. This technological shift represents a fundamental evolution in cloud management, offering enterprises unprecedented efficiency, reliability, and cost optimization across their distributed computing environments.
Developing Resilient Offline-First Architectures for Mobile Health and Clinical Research Applications
This paper presents an offline-first mobile architecture that is intended to be used in mobile health and clinical research applications, especially in places where the network is either intermittent or scarce. Since continuous data collection of patients is frequently used in clinical studies and other research projects of public health, the proposed system will produce smooth and safe clinical data collection, symptom monitoring, document uploading, and eligibility processes even during the demanding network conditions. The architecture allows using encrypted local storage, data versioning based on a time stamp, and conflict-aware synchronous mechanisms to ensure the integrity of the data and its adherence to regulatory requirements, such as HIPAA. The secure retry features are provided by the system which helps in the transfer of data reliably when the system is connected again. The mobile health research platforms case study illustrates sustained amelioration in participant adherence, loss of data, and strong longitudinal data collection especially in the under-serviced groups like the rural or medically isolated groups. This study will help to enhance digital health equity by addressing the issue of low-bandwidth clinical settings and offering a solution that is fault-tolerant. The offline-first architecture helps the mobile health applications to be resilient in order to support patient-reported outcomes (PROs) and clinical research processes and offer the compliant, efficient, and secure data management. The approach provides an entry to more believable, holistic and scalable mobile health and clinical research infrastructure
Secure Data Flow Patterns in Financial Integration Architecture
Financial integration architecture represents important infrastructure requiring strong safety controls to protect sensitive data, enabling efficient transaction processing. The complex scenario of financial technology includes systems spread from core banking platforms to customer-facing applications, each of which offers unique security challenges. This comprehensive examination of secure data flow patterns in financial integration architectures reveals the quantitative benefits of various architectural approaches, protection strategies, transport protocols, and implementation patterns. The evolution from traditional point-to-point connections toward hybrid and domain-specific architectures has yielded measurable security improvements while addressing performance considerations. Data payload protection through tokenization, field-level masking, and specialized encryption techniques demonstrates significant breach impact reduction across varied financial contexts. Transport-level security has matured from basic protocols toward multi-layered approaches incorporating mutual authentication and standardized API security. Implementation patterns tailored to specific integration scenarios—from CRM-to-ERP connections to cross-border transfers—provide frameworks for balancing security requirements with operational efficiency. The aggregated findings demonstrate that strategic selection and implementation of secure ntegration patterns yield substantial protection against evolving threats while enabling the flexibility required in modern financial ecosystems.
Context-Aware Rule Engines for Pricing and Claims Processing in Healthcare Platforms
The problem is that legacy rule engines in healthcare systems are rarely able to be flexible in real time, enable scale and make decision-making transparent. In this paper, the development of a context-aware rule engine to process pricing and claims is described on the basis of RESTful microservices and Spring MVC frameworks combined with caching technologies such as Couchbase. The engine has the capability of supporting upstream rule validation, contextual considerations at the session level and on-the-fly adjudication paths. The results reflect that the processing time, manual overrides and the costs of the operations have been reduced significantly. The architecture facilitates the payer-provider collaboration and compliance with improved auditability and transparency of decisions. The findings further highlight the fact that context-aware systems have the potential of transforming day-to-day business in the healthcare industry through the provision of intelligent and responsive moderately low-cost solutions to root out intractable claim and pricing logics.
Bridging the Digital Divide: Architecture for Equitable Technological Access
The digital divide has become one of the most urgent international issues, strengthening social, economic, and spatial inequalities by exposing some groups of people to disparate access to technological infrastructure, digital literacy, and inclusive design. This is a research article, Bridging the Digital Divide: Architecture to Provide Equitable Access to Technological Solutions that examines how architectural and urban design solutions can act as agents that encourage equitable access to digital technologies. The research places the construction not just as a physical site of presence of technology, but as a socio-technical interface which determines accessibility, usability and the involvement within various communities. The study takes a mixed-method research methodology, which entails qualitative and quantitative research methodology. First, a planned comparison of case studies of urban, peri-urban and rural settings analyzes digitally mediated spaces of community technology centers, libraries and hybrid civic buildings. Connectivity, adaptability and inclusivity are determined by spatial analysis and infrastructural mapping. Second, the interviews with the stakeholders (architects, planners, policymakers, and community users) give a patient insight into the lived experiences, design intentions, and barriers to access. Policy reviews and the analysis of demographic data supports these findings to identify the correlations of architectural interventions and outcomes of digital inclusions. The findings will prove that context-sensitive architectural design, which combines pliable spatial patterns, resilience infrastructure and community-focused programming, can be a major contributor in eliminating obstacles to technological access. The paper claims more the architectural task that makes spatial justice more akin to digital equity, suggesting the creation of a model that integrates technological access into the fundamentals of inclusive design. In the end, the research is relevant in the field of interdisciplinary discourse, which provides architecture as a strategic instrument in the fight against digital divide and promoting socially equitable technological futures
AI-Enabled Real-Time Financial Fraud Detection and Encryption Impact Analysis in High-Performance Cloud-Based Enterprise Networks
The rapid digitization of financial services and the widespread adoption of high-performance enterprise networks have significantly increased exposure to sophisticated and large-scale financial fraud. Traditional rule-based and batch-oriented security mechanisms are inadequate for detecting evolving fraud patterns in real time, particularly in environments characterized by high data velocity, encrypted traffic, and distributed cloud infrastructures. This paper presents an AI-enabled real-time financial fraud detection framework integrated with encryption impact analysis for high-performance enterprise networks. The proposed approach leverages machine learning and deep learning models to analyze streaming financial transactions while simultaneously assessing the computational and latency overhead introduced by cryptographic mechanisms such as symmetric encryption, public-key infrastructure, and secure key management. A hybrid architecture combining edge analytics, cloud-based intelligence, and secure data pipelines is introduced to ensure scalability, low-latency detection, and regulatory compliance. Experimental analysis demonstrates that the AI-driven model achieves high fraud detection accuracy with minimal performance degradation, even under strong encryption constraints. The results highlight a balanced trade-off between security, throughput, and response time, making the proposed framework suitable for modern enterprise and financial network environments requiring both real-time intelligence and robust data protection
The Evolving Role Of Oracle Cloud Dbas In The AI Era
The paper examines the role of Oracle Cloud Database Administrators (DBAs) shifting to the era of Artificial Intelligence and automation. Through a quantitative analytical approach, the study will focus on how the AI-driven tools at Oracle (e.g., Automated Database) have madethe work of employees less manual and generated new strategic tasks. The results indicate that AI has enhanced performance, uptime and cost efficiency and displaced DBA responsibilities to governance, compliance and data architecture. Other relevant skills identified in the study in cloud infrastructure, automation, and AI monitoring are new. The Oracle Cloud DBA profession in general is becoming more analytical, AI-facilitated, as well as business oriented.
Modernizing Legacy Applications for Cloud: Strategies and Lessons Learned
Legacy application modernization presents enterprises with significant challenges in transforming outdated monolithic systems into cloud-native architectures. This article explores proven strategies for successful cloud migration across architectural patterns, technical frameworks, organizational transformations, and risk mitigation approaches. Through examination of real-world implementations across multiple industries, the article identifies effective practices including the strangler fig pattern, domain-driven decomposition, and service extraction for architectural transformation; containerization, Kubernetes orchestration, API management, and serverless computing for technical implementation; product-oriented team structures, DevOps adoption, SRE practices, and skills development for organizational evolution; and phased migration, feature flags, parallel validation, rollback mechanisms, and business continuity planning for risk mitigation. Group findings provide actionable guidance for architects and engineering leaders who aim to maximize value by minimizing risk during the cloud modernization initiative.
GenAI & Healthcare APIs: Securing LLM Access to Sensitive Medical Records
The rapid adoption of generative artificial intelligence (GenAI) in healthcare has introduced transformative opportunities for patient engagement, clinical decision support, and administrative efficiency. Large language models (LLMs), when integrated with electronic health records (EHRs) and ancillary systems via secure healthcare APIs, can enable advanced conversational interfaces for patients, clinicians, and researchers. However, these integrations pose critical challenges related to data privacy, interoperability, and security. Sensitive patient records governed by HIPAA, GDPR, and other regulatory frameworks must be protected from unauthorized disclosure, particularly in scenarios where LLMs risk overexposure of data or manipulation through prompt injection attacks. This research explores a layered architecture for securing LLM access to healthcare APIs, focusing on three core areas: (1) limiting LLM data visibility through controlled API responses, (2) implementing robust defenses against prompt injection and adversarial queries, and (3) ensuring interoperability across heterogeneous systems via HL7–FHIR transformations. The proposed framework emphasizes zero-trust access models, de-identification techniques, and standardized data governance while highlighting practical use cases such as AI-enabled patient portals and clinical chatbots. Through conceptual modeling, threat analysis, and system design, this paper outlines best practices for balancing usability, interoperability, and compliance in GenAI-driven healthcare ecosystems.
Coding Influencer Follower and Engagement Analysis
With the rapid growth of social media platforms, coding influencers play a crucial role in disseminating programming knowledge, promoting tools, and shaping the learning behavior of aspiring developers. This paper presents an analytical study of coding influencers by examining their follower growth, engagement patterns, and content effectiveness across social media platforms. Using metrics such as likes, comments, shares, views, and engagement rate, the study identifies key factors that influence audience interaction. The analysis helps understand how content type, posting frequency, and platform choice impact engagement. The results provide insights for educators, developers, and marketers to optimize content strategies and enhance digital learning outreach.This research aims to conceptualize, develop, and validate a specific instrument for measuring the engagement of followers towards influencers on social media, and more specifically, in this first research, on Instagram. We surveyed (in-depth interviews, and questionnaires) 32 marketing experts and 1170 Instagram followers.Based on the applications of factor analysis and structural equation modelling, we determined 21 valid items. The scale assesses the cognitive, affective, and behavioral characteristics of follower’s engagement across five dimensions. The results provide insight into the interactive, personal, and social aspects of this type of virtual engagement. It is the first scale to measure this engagement in a multidimensional framework, which advances future research. Additionally, it will help managers identify the strongest dimensions of their influencers’ engagement and thus be able to adjust marketing communication strategies to foster multidimensional follower engagement and subsequent partnerships.
An Integrated Framework for Intelligent Healthcare and Industrial Systems using AI and SDN NFV Enabled Cloud Network Architectures and Privacy Preserving Security
The rapid evolution of intelligent healthcare and industrial systems has introduced complex challenges related to scalability, reliability, security, and data privacy. Emerging technologies such as artificial intelligence (AI), software-defined networking (SDN), network function virtualization (NFV), and cloud computing offer promising solutions, yet their isolated adoption limits overall system effectiveness. This paper proposes an integrated framework that unifies AI-driven analytics, SDN/NFV-enabled cloud networks, and privacy-preserving security mechanisms to support intelligent, scalable, and resilient healthcare and industrial applications. The framework enables real-time decision-making, adaptive network management, secure data exchange, and efficient resource utilization across heterogeneous cyber-physical environments. AI models enhance predictive analytics, anomaly detection, and operational optimization, while SDN and NFV provide flexible and programmable network control. Privacy-preserving security techniques ensure data confidentiality and integrity without compromising system performance. The proposed architecture is designed to handle dynamic workloads, mission-critical communication, and large-scale data processing demands. This study contributes a holistic approach that addresses both technological and operational requirements, offering a foundation for next-generation intelligent systems capable of supporting healthcare delivery, industrial automation, and critical infrastructure services in cloud and 5G-enabled environments.
An Integrated Artificial Intelligence and Cloud Ecosystem for Secure Web Application Development and Healthcare Imaging over 5G Networks
The convergence of Artificial Intelligence (AI), cloud computing, secure web application development, and 5G networking is drastically reshaping digital ecosystems, particularly in domains that demand high performance and robust security such as healthcare imaging and large-scale web platforms. This paper presents an integrated ecosystem designed to leverage the elasticity and scalability of cloud infrastructure with intelligent AI-driven analytics, secured web frameworks, and the ultra-low latency and high bandwidth afforded by 5G networks. By combining these technologies, the ecosystem enables rapid, secure processing of complex medical images, supports advanced predictive services, and enhances the resilience of web applications against emerging cyber threats. Key design principles include modular microservices, containerized deployment, end-to-end encryption, federated learning for privacy preservation, and DevSecOps practices for continuous security reinforcement. Empirical evidence highlights improvements in diagnostic turnaround times, accuracy of automated image interpretation, response times in web services, and adaptive threat detection. Challenges related to data governance, algorithmic bias, computational cost, and secure model deployment are examined. The ecosystem’s implications for future digital infrastructures indicate profound opportunities for scalable, secure, and intelligent distributed systems across industries.
Resilience Engineering Principles for Distributed Cloud-Native Applications under Chaos
The concepts of resilience engineering and chaos engineering are presented in this paper in the framework of the development of fault-tolerant distributed applications based on cloud-native technology. The paper outlines the design patterns of the system to enhance its reliability that can be achieved by proactive patterns of designs like circuit breakers, bulkhead patterns, and dynamic retry patterns. It introduces a systematic approach to failure injection testing where the system is supposed to withstand some disruption expecting the unexpected in the system without failure. The architecture proposed is based on decoupling services using circuit breakers that eliminate cascading failures and on the use of bulkhead patterns, which confine the number of failures to certain components, so that the whole system does not suffer. Adaptive retry schemes which dynamically adapt to network and service availability are also there. The platform also promotes graceful degradation to allow the application to be still used with a lower level of functionality in the event that certain components fail, and a user experience is still tolerable. Chaos engineering, in its turn, introduces failures to replicate the reality and stress test system resilience. The paper is an elaborated instructional guide to engineers and developers on how they can develop a high-resilience, fault-tolerant cloud-native application that can endure and recover failures without triggering any major service outage
ISO 20022 Migration at Scale: Architectural Patterns and Engineering Challenges in Modernizing Global Financial Messaging Infrastructure
The global financial services industry is undergoing one of its most significant technical transformations with the migration from legacy MT (Message Type) formats to ISO 20022 XML-based messaging standards. This transition affects financial messaging networks processing $5-7 trillion in cross-border payments daily, encompassing over 11,000 financial institutions across 200+ countries. A critical challenge in this migration is that not all institutions can simultaneously upgrade their systems to send ISO 20022 (MX) messages, creating a need for transparent MT-to-MX conversion capabilities. This paper presents the design, implementation, and operational experience of a production-grade MT-MX conversion system that enables seamless migration while maintaining continuous infrastructure operation. The author's direct involvement in developing the conversion engine, managing production rollout, debugging complex translation scenarios, and providing technical support for 11,000+ institutions provides unique insights into large-scale financial infrastructure transformation. This work demonstrates how intelligent format conversion serves as the foundational enabler for zero-downtime migration of critical payment systems
From Data to Decisions a Unified AI Driven Framework for Retail Forecasting and Enterprise Operations and Cybersecurity Resilience
Enterprises operating in data-intensive environments increasingly rely on artificial intelligence to transform raw data into actionable decisions. Retail organizations, in particular, face the combined challenge of accurately forecasting demand, optimizing enterprise operations, and maintaining resilience against escalating cybersecurity threats. This paper proposes a unified AI driven framework that integrates retail forecasting, enterprise operational intelligence, and cybersecurity resilience within a single decision-oriented architecture. The framework combines advanced analytics, machine learning, and contextual intelligence to enable real-time, adaptive, and risk-aware decision-making. By unifying business data, operational metrics, and security signals, the proposed approach supports proactive forecasting, efficient resource utilization, and resilient enterprise operations. Cybersecurity intelligence is embedded directly into the decision framework to ensure that operational and strategic decisions account for evolving threat landscapes. The framework emphasizes scalability, explainability, and governance to support trustworthy AI adoption in complex enterprise environments. This research contributes a holistic architectural and methodological perspective on how AI-driven systems can bridge the gap between data generation and enterprise decision-making, enabling organizations to transition from reactive analytics toward resilient and intelligent decision ecosystems.
Retail Forecasting, Enterprise Operations
Establishing Auditable and Privacy-Respectful Test Data Systems through Synthetic Data Engineering and Governance-Driven Anonymization
Test data plays a critical yet often underestimated role in enterprise system development, quality assurance, and regulatory validation, particularly in environments where production data is protected by strict privacy and compliance obligations. This study argues that traditional masking based approaches are insufficient to address emerging demands for auditability, accountability, and sustained regulatory trust within test data ecosystems. Instead, it advances a governance led perspective in which synthetic data engineering and anonymization are treated as coordinated control mechanisms rather than isolated technical utilities. The paper develops a structured framework for establishing auditable and privacy respectful test data systems by aligning synthetic data generation processes with formal governance policies, role based oversight, and traceable anonymization decisions. Through an analytical synthesis of established privacy principles, data governance practices, and test environment management strategies, the study demonstrates how synthetic datasets can preserve functional realism while reducing exposure to sensitive attributes. It further examines how governance driven anonymization enables consistent enforcement of privacy constraints, supports compliance verification, and facilitates transparent audit review without compromising development velocity. Empirical patterns drawn from enterprise data management practices suggest that embedding auditability directly into test data workflows improves organizational confidence, reduces regulatory risk, and strengthens long term data stewardship maturity. By reframing test data as a governed asset rather than a disposable byproduct of development, this research contributes a foundational reference model that can inform both academic inquiry and enterprise implementation. The findings position governance integrated synthetic data engineering as a practical pathway toward resilient, compliant, and trustworthy test data systems suitable for regulated operational contexts.
Privacy-Preserving AI-Enabled Cloud Lakehouse Ecosystem using Blockchain and Machine Learning for Secure SAP Web Applications and Medical Imaging Analytics
The rapid adoption of cloud-based data platforms and artificial intelligence (AI) has transformed enterprise applications and healthcare analytics. However, concerns related to data privacy, security, regulatory compliance, and interoperability remain significant challenges, particularly for sensitive domains such as SAP enterprise systems and medical imaging. This paper proposes a privacy-preserving AI-enabled cloud lakehouse ecosystem that integrates blockchain technology and machine learning to ensure secure, transparent, and compliant data management. The proposed architecture leverages blockchain for immutable audit trails, decentralized access control, and data provenance, while machine learning models enable intelligent analytics and automation. The lakehouse paradigm unifies structured and unstructured data processing, supporting real-time SAP web applications and large-scale medical imaging analytics. Privacy-enhancing techniques such as encryption, federated learning, and role-based access control are incorporated to meet regulatory requirements including HIPAA and GDPR. Experimental evaluation demonstrates improved data security, system scalability, and analytics performance compared to traditional cloud architectures. The proposed ecosystem provides a robust framework for secure digital transformation across enterprise and healthcare domains.
Transforming Enterprise Transaction Data into Intelligent Decision Systems
The digital change of enterprise transaction processing offers an unprecedented opportunity for implementing artificial intelligence capabilities into enterprise-level business operations. This article discusses the dimensions of architecture, implementation technology, and strategic benefits of moving from raw transaction data to intelligent decision-making systems. As enterprises move away from batch-oriented systems and towards real-time streaming architectures, organizations can ingest vast amounts of transaction data while achieving sub-millisecond latencies with exceptional accuracy rates in fraud detection, customer personalization, and predictive analytics. Enterprise transaction systems require diverse architectural components to address the complexity of data ingest layers, stream processing engines, feature extraction pipelines, and machine learning orchestration platforms to facilitate the most seamless user experiences possible. Real-world enterprise implementations have demonstrated substantial operational efficiencies, increased value-for-money, and improved customer satisfaction through automated decision- making in transactions. The opportunity for strategic advantage extends beyond operational efficiencies to incorporate longer-term competitive differentiation through high-quality decision-making, personalized customer experiences, and adaptive awareness that allows for continual adjustment of the business model to align with changes to the market or customer actions.
Automated Migration Frameworks for Legacy Systems: A Security-Driven Approach
The article outlines a computerized system of migration that will modernize the outdated enterprise integration gateways using MuleSoft Anypoint Platform. The proposed architecture will apply reusable migration patterns, standardized API proxy patterns, and automated Continuous Integration/Continuous Deployment (CI/CD) pipelines to assist in the transformation of the old system to API-led systems. Policy-based security enforcement is another differentiating feature of the framework that is made possible by a centralized form of governance, thereby ensuring that all the assets that have been migrated have similar authentication and authorization and compliance controls. The framework assists in the enhancement of the scalability, maintenance, and adherence to the regulations since it isolates security policies and the application logic. This will minimize downtime and risk of operation, particularly when engaging in massive modernization programmes. Security protocols incorporated in the migration process will be implemented so that the legacy systems are not only modernized, but they are also secure and in line with industry regulations. All in all, the framework proposes a methodical process of migrating the legacy systems to more scalable, flexible, and secure API-based environments, and the advantages in terms of operational efficiency and long-term sustainability are enormous
Fiber Broadband for Big Data–Powered Secure SAP Cloud Architecture using Machine Learning in Healthcare
The convergence of high-capacity fiber broadband with next-generation 5G wireless networks represents a pivotal architectural transformation for achieving intelligent, secure cloud connectivity. Fiber broadband delivers unparalleled bandwidth and reliability, while 5G provides low latency, high throughput, and ubiquitous wireless access. Integrating these technologies supports a range of emerging applications—such as edge computing, autonomous systems, Internet of Things (IoT), and mission-critical services—requiring seamless, high-performance links to cloud infrastructure. This research explores the technical, architectural, and security implications of integrating fiber broadband and 5G networks, focusing on cloud-centric connectivity solutions that enhance performance, scalability, and trustworthiness. The study synthesizes existing literature to highlight key integration models, evaluates performance and security challenges, and proposes a methodology for assessing the benefits of convergence. The findings demonstrate that integrated fiber–5G solutions significantly improve throughput, reliability, and service quality while enabling advanced features such as network slicing, edge orchestration, and secure multi-access strategies. However, challenges remain, including infrastructure costs, interoperability complexity, and end-to-end security coordination. The research concludes with recommendations for deployment best practices and future research directions to advance intelligent, secure cloud connectivity powered by fiber broadband and 5G convergence.
Hybrid Integration Platforms in the Enterprise: Architectural Strategies for Bridging API Gateways and Legacy SOA Systems
Hybrid Integration Platforms (HIPs) have become a critical architectural component for modern enterprises seeking to balance digital transformation with the continued reliance on legacy Service-Oriented Architecture (SOA) systems. As organizations increasingly adopt API-driven and cloud-native architectures, the challenge lies in seamlessly integrating modern API gateways with established enterprise systems such as ESBs, SOAP-based services, and mainframe applications. This article explores architectural strategies for bridging API gateways and legacy SOA environments using hybrid integration platforms. It examines key design patterns, integration styles, governance models, security considerations, and operational challenges involved in achieving interoperability across heterogeneous systems. The paper also discusses performance optimization, scalability, fault tolerance, and migration strategies that enable enterprises to modernize incrementally without disrupting mission-critical systems. Through architectural analysis and illustrative diagrams, this work provides a comprehensive framework for designing robust hybrid integration solutions that support agility, scalability, and long-term enterprise integration goals.
The Role of Digital Twins in Supply Chain Process Simulation and Optimization
Supply chains are increasingly exposed to disruption, demand volatility, and operational complexity that traditional planning tools cannot fully capture in real time. Digital twin technology is emerging as a powerful capability for process simulation and optimization by creating a continuously updated virtual representation of supply chain operations. A digital twin combines physical process data, enterprise system transactions, and predictive analytics to simulate scenarios, evaluate trade-offs, and recommend optimal decisions before execution. This paper explores the role of digital twins in improving supply chain performance through simulation-based planning, bottleneck identification, inventory optimization, transportation network design, and resilience building. The study synthesizes research and industry frameworks to evaluate enabling technologies, implementation approaches, governance requirements, and barriers such as data quality, integration complexity, and organizational change. The findings indicate that digital twins can deliver measurable improvements in service levels, throughput, and cost efficiency by enabling decision-makers to evaluate outcomes under multiple conditions, including disruptions. However, value realization depends on strong master data, integration with ERP and IoT sources, and continuous model validation. The paper concludes with recommendations for implementing digital twins as an operational capability rather than a one-time transformation project.
Intelligent and Secure SAP AI Solutions for Optimized Cloud Network Performance in Healthcare and Digital Advertising
The rapid adoption of cloud-based enterprise platforms has increased the demand for intelligent, secure, and high-performance network infrastructures, particularly in data-intensive domains such as healthcare and digital advertising. This paper presents an intelligent and secure SAP AI–driven solution for optimizing cloud network performance while ensuring data privacy, regulatory compliance, and operational efficiency. The proposed framework integrates SAP Business Technology Platform (BTP), AI-enabled analytics, and secure cloud networking mechanisms to dynamically monitor, predict, and optimize network traffic, latency, and resource utilization. Machine learning models are employed to analyze real-time and historical network data, enabling proactive congestion management, adaptive bandwidth allocation, and anomaly detection. In healthcare environments, the solution enhances secure data transmission for electronic health records and telemedicine applications, while in digital advertising it improves campaign delivery, real-time bidding performance, and audience analytics. Security is reinforced through AI-assisted threat detection, policy-driven access control, and encrypted data flows across multi-cloud environments. Experimental analysis demonstrates improved network efficiency, reduced latency, and enhanced security resilience compared to traditional cloud network management approaches. The proposed SAP AI-based solution offers a scalable and future-ready architecture for intelligent cloud network optimization across mission-critical industries.
High-speed printed circuit board (PCB) design has become increasingly complex due to rising data rates, dense component integration, and stringent signal and power integrity requirements. Traditional PCB design approaches rely heavily on manual expertise and iterative trial-and-error methods, which often lead to prolonged design cycles and suboptimal performance. Recent advances in Artificial Intelligence (AI) and machine learning provide new opportunities to automate and optimize high-speed PCB design processes. This paper presents an AI-assisted framework for high-speed PCB design that integrates machine learning techniques into the conventional design flow to address signal integrity, electromagnetic interference, and routing optimization challenges. The proposed methodology leverages design data and electrical constraints to train intelligent models capable of predicting optimal routing strategies and layout parameters. Experimental evaluation demonstrates that the AI-assisted approach significantly improves signal integrity performance while reducing design iterations and development time when compared with conventional design techniques. The results highlight the potential of AI-driven automation in enhancing PCB design efficiency and reliability, making it a promising solution for next-generation high-speed electronic systems.
Privacy-Preserving SAP-Based Analytics for Healthcare Applications and Wastewater Management Using LLMs and Cloud Encryption
The rapid adoption of SAP-based enterprise systems in financial and healthcare sectors has intensified concerns related to data security, privacy preservation, and regulatory compliance. The integration of Large Language Models (LLMs) into cloud-enabled SAP environments introduces powerful capabilities for intelligent automation, decision support, and business process optimization, while simultaneously expanding the attack surface. This study proposes a secure and privacy-preserving framework that combines LLM-driven intelligence with robust cloud encryption mechanisms to protect sensitive financial and healthcare data throughout its lifecycle. The architecture incorporates end-to-end encryption, role-based access control, secure key management, and policy-aware LLM orchestration to ensure compliance with standards such as HIPAA, GDPR, and PCI-DSS. By enabling encrypted data processing and controlled semantic inference, the framework minimizes data exposure while maintaining operational efficiency and analytical accuracy. Experimental evaluation demonstrates improved threat detection, reduced data leakage risk, and enhanced system resilience without significant performance overhead. The proposed approach highlights the feasibility of deploying LLMs in mission-critical SAP environments while achieving strong security guarantees and data privacy assurance.