American International Journal of Computer Science and Technology
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Enterprise Architecture Framework for Secure Large Language Model Deployment in Financial Services
The rapid advancement of Large Language Models (LLMs) has created new opportunities for financial institutions to enhance customer engagement, investment research, advisor productivity, and operational efficiency. However, enterprise adoption of LLMs remains challenging due to stringent regulatory requirements, data privacy concerns, cybersecurity risks, model governance complexities, and integration with existing enterprise systems. While existing research primarily focuses on LLM capabilities and model performance, limited attention has been given to a comprehensive enterprise architecture that enables secure, scalable, and governed deployment of LLMs within regulated financial environments. This paper proposes a novel Enterprise Architecture Framework for Secure Large Language Model Deployment in Financial Services to support the responsible adoption of generative AI across banking, asset and wealth management, and capital markets. The proposed framework consists of six vertically integrated architectural layers—Experience and Channels, AI Orchestration and Policy, LLM Services, Knowledge and Retrieval-Augmented Generation (RAG), Enterprise Integration, and Platform and Deployment—supported by four cross-cutting control domains: Security, Governance, Compliance, and Observability. These layers provide a scalable foundation for integrating LLM capabilities into enterprise applications while ensuring regulatory compliance, data protection, explainability, and operational resilience. A design science research methodology is employed to develop and validate the framework using a representative enterprise financial services case study. The evaluation demonstrates how the proposed architecture enables secure knowledge retrieval, intelligent research assistance, workflow automation, and decision support while maintaining identity-based access control, human oversight, auditability, and responsible AI governance. By combining cloud-native architecture, API-driven integration, zero-trust security, and continuous monitoring, the framework addresses critical enterprise requirements for secure LLM adoption. The proposed framework contributes a reusable enterprise reference architecture that bridges the gap between generative AI innovation and the operational, security, and governance demands of modern financial institutions, providing enterprise architects and technology leaders with practical guidance for implementing trustworthy and scalable LLM solutions.
Enterprise CRM Modernization for Asset and Wealth Management: A Capability-Driven Digital Transformation Framework
In the world of assets and wealth management, CRM systems are key to multiple digital processes, including onboarding, servicing customer relationships, managing portfolios, complying with regulations, and improving advisor collaboration. However, a large number of financial institutions are still running legacy CRM systems that have evolved over time with incremental changes, leaving their customer data disparate and not fully integrated with enterprise applications, workflows inconsistent, duplicate records prevalent and operations increasingly complex. Limitations lead to lower agility, higher maintenance and compliance expenses, and restrict companies to providing timely, customer-centric services in a highly mediated environment. This paper will present an enterprise capability-driven digital transformation framework that encompasses technology change with enterprise capability and enterprise architecture principles and operational goals. The framework organizes essential skill sets from client onboarding through relationship management, portfolio servicing, compliance management and reporting and workflow automation components into hierarchical layers and structures that lead up to a presentation piece, supported by a business services layer, an integration layer, a data management layer and a governance layer. The approach highlights the importance of standardised processes, centralised customer data, service-oriented integration, gradual implementation and robust governance oversight, which helps to enhance data quality, operational efficiency, and system interoperability and minimise implementation risk. The study clearly shows the correlation between appropriate CRM modernization and the ability to achieve sustainable and scalable approaches to digitalization for asset / wealth management organisations.
Enterprise Technology Engineering 360°: A Unified Reference Model for Requirements Engineering, Solution Architecture, Product Delivery, Infrastructure Operations, Client Engagement, and Team Leadership
The contemporary enterprise technology landscape presents a formidable challenge: the fragmentation of specialized disciplines into isolated silos. Requirements engineering operates in relative isolation from solution architecture; product delivery teams pursue velocity while infrastructure operations prioritize stability; client engagement focuses on satisfaction while team leadership emphasizes execution. This fragmentation produces misaligned priorities, suboptimal outcomes, and organizational friction. This research paper presents the Enterprise Technology Engineering 360° (ETE-360) Reference Model, a comprehensive unified framework that integrates six critical domains: Requirements Engineering, Solution Architecture, Product Delivery, Infrastructure Operations, Client Engagement, and Team Leadership. Drawing upon established enterprise architecture frameworks including TOGAF, Zachman, and FEAF, IT Service Management best practices including ITIL and ISO/IEC 20000, and reference modeling methodologies, this research constructs a holistic model that addresses the interdependencies and overlaps inherent in enterprise technology delivery. The model provides a structured approach to aligning business objectives with technical execution, establishing clear accountability boundaries while recognizing essential cross-domain interfaces. The research methodology follows established reference model construction processes, encompassing problem definition, planning, model construction, validation, and practical application considerations. The ETE-360 model contributes to both academic discourse and practical enterprise technology management by offering a unified conceptual framework that reduces ambiguity, enhances communication, and enables more effective technology engineering outcomes. This paper presents the theoretical foundations, structural components, and practical implications of the ETE-360 Reference Model, establishing a foundation for future empirical validation and refinement.
Data-Centric AI in the Era of Large Volumes: Improving Model Outcomes through Data Quality Engineering
Nowadays, the whole AI system’ success is determined not only by algorithms but also, and even more importantly, by the data quality that the systems use. The rise in the amount, speed, and diversity of data has reached the point where it is no longer possible to rely only on a model-centric approach. This article tracks the extent of the growth of data-centric AI which addresses the improvement of the data itself as the main lever for better model outcomes. At the core of this transformation lies data quality engineering—a highly disciplined and oftentimes neglected profession that goes way beyond performing the research on datasets and running formal verification tests, but it is basically the very essence of completeness, consistency, and context appropriateness of the datasets in question prior to their being used in any modeling work. In high-volume environments that are prone to falling, minor inconsistencies may become a big headache and using the preventative data hygiene approach in this context becomes indispensable. The paper looks into ways of how implementing data quality pipelines, anomaly detection, labeling integrity checks, and feedback loops into AI workflows not only makes models more reliable but also minimizes retraining costs and shortens deployment cycles. The embedding of data quality engineering into the development lifecycle has facilitated organizations™ transition from being reactive to the failures to a culture of continuous improvement driven by trusted data. This transformation allows teams to focus more on innovation and less on debugging. In the final analysis, this mind shift paper argues that in the world of such a massive increase of data, a data-centric approach—rooted in engineering discipline—is the most effective way of achieving scalable and robust AI.
Jupyter Notebooks as First-Class Citizens in Cloud-Native Data Workflows
Jupyter Notebooks have quickly become must-have tools for engineers, analysts, and data scientists. They provide a flexible, interactive platform that combines code, visualization, and story in one place. However, even though they are widely used for exploration and experimentation, they have traditionally had trouble fitting into production-grade, collaborative, and scalable data processes. This research looks at how Jupyter Notebooks might be reimagined as the vital parts of cloud-native ecosystems, going beyond their limits to become more important parts of these modern data infrastructure. We look at the latest ways of building and running things that make this change possible, such as CI/CD pipelines, Kubernetes orchestration, and seamless connections with these containerized environments. The goal is to make notebooks more reliable, secure, and scalable, turning them from solo scripts into useful, collaborative tools. We look at how tools like Papermill, JupyterHub, and Kubeflow Notebooks make production more ready while still meeting governance and these compliance needs. A case study shows how a corporate data platform may be useful in actual life and how to put it into action. This point of view stresses how cloud-native notebook methods make it easier for teams to work together, speed up model deployment cycles & create a culture of open, reproducible analytics. The report says that Jupyter Notebooks are not just a way to program, but they are also an important part of the modern cloud-based data and ML pipelines.
AI-Augmented Data Governance: Enabling Intelligent Access, Lineage, and Compliance across Hybrid Clouds
In today's data ecosystem, businesses are increasingly relying on their hybrid cloud environments to store & manage their information assets. However, this flexibility means that data governance has to be more thorough & smart. AI-enhanced data governance changes the way we think about data governance by adding these smart technologies like machine learning, natural language processing (NLP) & advanced metadata analytics. These tools work together to automate & improve basic governance tasks like keeping an eye on where information comes from, controlling who can access it & making sure the rules are followed. They do all of this while keeping in mind how flexible their hybrid infrastructures are. AI is a better approach to look at these types of hazards, find new issues & enforce rules in actual time, rather than only relying on their human supervision or rule-based systems. Machine learning (ML) can discover abnormal access patterns that might indicate a security breach & natural language processing (NLP) can look at policy papers that aren't organized to determine how they fit with the company's internal controls. Metadata analytics makes this ecosystem better by giving these organizations deep insights into data flows. This makes it easy for them to answer questions like "Where did this data come from?" or "Who changed it and when?" Actual world examples from healthcare, finance & retail show that AI-driven governance not only makes it easier to follow the rules and lowers the risk of data misuse, but it also gives data stewards and business users useful information that helps them make better decisions. The outcome is a governance architecture that is proactive, intelligent & scalable, rather than reactive and laborious. This meets the needs of modern corporate data strategy. In the end, AI-enhanced data governance builds a culture of trust, flexibility & accountability in these hybrid clouds. This makes operations safer, speeds up innovation, and helps people make better, more data-driven decisions.
Edge-Cloud Hybrid Data Pipelines: Architectures for Federated Analytics and Learning
The paper discusses the importance of seamless integration between edge and cloud systems in edge-cloud hybrid data pipelines for the growing role of federated analytics and machine learning. The conjunction of edge computing (hardware near the data source) with the cloud (that has scalable resources) has allowed the definition of new data analysis methods at scale, in real time and with a higher respect for privacy. Those hybrid approaches in federated learning and analytics scenarios can allow the information to remain scattered, which means that the safety and the respecting of the rules can be improved. Also, not only do they still allow the global insight or the coordinating but in addition, the model training and the analytics orchestration are going on. The paper shows that the architectures have to support continuous safe & low-latency data flows through a wide variety of edge nodes and cloud platforms during these network conditions that change. Real-time decision-making, anomaly detection, and adaptive model retraining are just a few examples of applications that benefit from this synergy. Several architectural patterns are dissected in this paper to illustrate the design trade-offs in such parts as workload distribution, data synchronization, orchestration, and trust boundaries. We also touch on the employment of containerization, message queues, federated model aggregation, and privacy-preserving mechanisms to ensure that these data pipelines remain scalable, resilient, and secure. In the end, this work lays down a detailed framework for designing edge-cloud hybrid systems for carrying out intelligent, federated operations, which units are thus able to effectively manage distributed data while still providing the same levels of performance, security, and regulatory compliance.
From Monolithic Systems to Cloud-Native Ecosystems: Modernizing Enterprise Applications with Kubernetes and Microservices
Modernizing corporate applications is a strategic imperative for enterprises to stay competitive in a more digital and data-driven world. Traditional monolithic systems have serviced intricate corporate activities well in the past but often don’t fit today’s requirements for scalability, agility, resilience and speed of invention. Because they are tightly coupled, they are more complex to build, deploy and administer, which raises operational costs and hinders their response to changing business needs. Cloud-native computing has changed how we build and run programs to be elastic, automated and make the best use of resources in distributed environments. In this context, microservices architecture has developed as a major notion to decompose programs into smaller independently deployable services, which can enhance scalability, fault isolation and development efficiency. Kubernetes has evolved as the standard for automating the deployment, scaling, networking, and lifecycle management of containers. It offers an efficient solution for organizations to operate complex application ecosystems. In this paper we focus on the shift from monolithic business systems to cloud-native ecosystems based on Kubernetes and microservices. This article provides a comprehensive survey of the existing research, industrial approaches and real modernization activities to assess the architectural, operational and organizational impacts of the adoption of cloud-native technology. We study the impact of modernization methodologies, deployment strategies and implementation challenges on the performance, scalability, reliability and business agility of the system. Results indicate that organizations adopting microservices design on Kubernetes can achieve higher deployment frequency, better resiliency, improved resource utilization and greater agility to respond to evolving market needs. The change introduces problems in service management, security, observability and operational complexity that need to be planned and regulated in detail. The paper provides a broad view on enterprise modernization, including important success factors, best practices and architectural considerations for organizations embarking on cloud native transformation programs. The analysis shows that Kubernetes and microservices are the crucial technologies to construct sustainable, scalable and future-proof enterprise application ecosystems.
Achieving Timing Closure in Advanced ASIC and FPGA Systems: Challenges, Methodologies, and Best Practices
Timing closure is one of the most critical challenges in the design of modern digital integrated circuits. This is primarily because semiconductor technologies are continuously scaling to deep submicron and nanometer process nodes. New Application Specific Integrated Circuits (ASICs) and Field-Programmable Gate Arrays (FPGAs) have reached almost unbelievable levels of performance, integration, and functionality, that is on the one hand. New timing problems have arisen from these developments on the other hand, making the work of designers very difficult to the extent that setting up and holding times, clock distribution, signal integrity, and overall performance achievement of the design through timing are no longer issues which can be done easily. The complexity of timing closure is further compounded by various factors like process, voltage, and temperature (PVT) variations, increasing interconnect delays, very low-power budgets, high-frequency operation, and the increasing use of heterogeneous architectures combining different types of functional blocks. To tackle these problems, designers use a variety of techniques such as static timing analysis, timing-driven synthesis, clock tree synthesis, placement and routing optimization, constraint management, and advanced signoff (final verification) techniques. In this article, the authors provide a detailed description of how timing closure is performed in advanced ASIC and FPGA systems and introduce a well-structured framework that combines design optimization, timing analysis, and iterative verification to enhance convergence and minimize implementation risks. To give readers a better understanding of the proposed framework, a real-life example is presented demonstrating the use of the framework for fixing timing violations while keeping design performance, power efficiency, and area utilization. The authors' experience shows through the example that one should use a systematic and data-driven timing closure methodology throughout the entire design cycle. Major results show that early timing knowledge, correct constraint definition, and ongoing optimization are the key factors that greatly improve timing predictability and overall design quality. The paper provides a comprehensive overview of the issues related to timing closure as well as a detailed guide on the implementation of best practices. It can serve as an extremely useful reference for anyone who is involved in the design of next-generation high-performance ASIC and FPGA platforms.
A Multi-Tier Orchestration Architecture for Regulated Cryptocurrency and Stablecoin Services within Large-Scale Payment Service Providers
Cryptocurrencies and regulated stablecoins will come with compliance, scalability, resilience, liquidity management, and interoperability, among other architectural challenges in the mainstream payment service provider (PSP) environment. Conventional payment systems are geared toward deterministic settlement and central control while the digital asset system is built using distributed network, partner, custody models, and changing regulatory frameworks. The purpose of this paper is to propose a multi-tier orchestration architecture that supports regulated cryptocurrency and stablecoin services in large-scale PSP environments, while keeping the vendors neutral. The architecture is based on 5 logical layers: client channels, experience and API services, transaction orchestration, digital asset core services and shared data infrastructure. An event-driven processing model allows to execute transactions on a scale, maintain regulatory traceability, and to integrate asynchronously with distributed services. It also features a standardized Outbound Connector Framework to connect custodians, market data vendors, compliance services, and tax platforms, alongside a Liquidity Pool Management architecture to enable treasury management, stablecoin conversion, settlement checks and constant reconciliation. It is important to note that the architectural analysis emphasizes regulatory compliance, operational resilience, flexibility, audibility and the ability to support the development of digital asset ecosystems. This model offers a good template for both PSPs and neobanks, and also for merchant platforms aiming to provide regulated digital asset services.
Retrieval-Augmented Generation for Trusted Customer Intelligence: A Scalable Enterprise Architecture for CRM Knowledge Management
Enterprise customer relationship management systems increasingly operate as repositories of fragmented customer knowledge rather than as unified intelligence platforms. Sales representatives, service agents, customer-success teams, marketing analysts, and executives rely on CRM data, call transcripts, support tickets, contracts, product documentation, knowledge articles, renewal histories, and external market signals, yet these assets are often distributed across incompatible schemas, permissions, and narrative formats. Large language models offer new opportunities for conversational customer intelligence, but their direct use in CRM environments introduces unacceptable risks, including hallucinated recommendations, privacy exposure, stale knowledge, weak auditability, and limited alignment with enterprise decision governance. This paper proposes a scalable retrieval-augmented generation architecture for trusted customer intelligence in enterprise CRM knowledge management. The proposed framework integrates hybrid retrieval, semantic indexing, policy-aware document ingestion, customer-entity resolution, vector and graph-based knowledge representations, evidence-grounded generation, human-in-the-loop feedback, and trust-oriented observability. The architecture is designed to support account planning, opportunity qualification, customer service resolution, churn-risk explanation, sales enablement, executive decision support, and knowledge reuse across customer operations. A design-science methodology is adopted to formalize the artifact, decompose architectural layers, define trust controls, and establish evaluation metrics for retrieval quality, answer faithfulness, decision usefulness, latency, scalability, compliance, and user adoption. The analytical discussion shows that enterprise RAG is not merely an LLM enhancement pattern but a socio-technical knowledge-governance architecture that transforms CRM from a passive transaction system into a source-grounded intelligence environment. The study contributes a conceptual model, evaluation framework, and implementation roadmap for organizations seeking to deploy generative AI in customer-facing functions without compromising factual reliability, security, or managerial accountability.
Counterfactual Telemetry Simulation for Release-Aware Capacity Guardrails and Performance Risk Mitigation in Microservice Architectures
Modern microservice architectures enable rapid release velocity, elastic deployment, and fine-grained service ownership, yet they also introduce difficult performance-governance problems. A single release may alter service latency, resource demand, fan-out behavior, queue depth, retry amplification, downstream saturation, or autoscaling stability in ways that are not visible during conventional pre-production testing. Existing observability and autoscaling mechanisms remain largely reactive: they detect degradation after telemetry has already shifted, after error budgets have begun burning, or after horizontal scaling decisions have lagged behind workload growth. This paper proposes a counterfactual telemetry simulation framework for release-aware capacity guardrails and performance risk mitigation in microservice architectures. The framework combines distributed traces, metrics, logs, release metadata, workload features, dependency graphs, and capacity-state information to estimate what system behavior would likely look like under alternative release and capacity scenarios. Instead of asking only whether the current deployment is healthy, the proposed approach asks whether a new release would remain safe under plausible workload, dependency, and resource conditions. The paper develops a conceptual model, methodological pipeline, evaluation criteria, and analytical discussion for applying counterfactual simulation to release gates, canary expansion, autoscaling policy tuning, and service-level objective protection. The main contribution is a release-aware guardrail architecture that transforms telemetry from retrospective evidence into prospective operational intelligence. The proposed framework is particularly relevant for Kubernetes-based cloud-native systems where service versions, autoscaling thresholds, resource limits, and traffic splits evolve continuously. The paper argues that counterfactual telemetry simulation can reduce performance risk by improving pre-release capacity reasoning, detecting unsafe release-resource interactions, and supporting explainable mitigation actions before user-facing degradation occurs.
From Legacy Back Office to Intelligent Utility Enterprise a Practitioner Case Study of SAP Cloud Transformation and Utility IT Landscape Modernization
Utilities operate complex, regulated, and legacy-heavy technology estates in which modernization must improve agility without compromising reliability, controls, or operational continuity. This practitioner case study describes how a large energy utility moved from a fragmented back-office environment toward a cloud-based SAP digital core supported by cloud line-of-business applications, a standardized integration backbone, and a real-time data foundation. The landscape is described explicitly in utility terms: customers, employees, suppliers, field workforce, regulators, and utility operations on one side; SAP cloud applications, integration governance, the SAP S/4HANA digital core, SAP HANA, and SAP Cloud Platform analytics capabilities, and future digitization capabilities such as connected assets, omni-channel service, and automation on the other. The paper contributes a reusable visual architecture pattern and a staged roadmap for utilities planning SAP cloud modernization. Its central argument is that the program's durable value came not only from migrating applications but also from deliberately designing integration, data flow, security, supportability, and operating controls around the digital core.
Original Article Scalable Enterprise Intelligence: Leveraging AI, Data Engineering, Security, and Automation for Digital Transformation
The rapid evolution of digital technologies has transformed the operational landscape of modern enterprises, compelling organizations to adopt intelligent, scalable, and secure data-driven architectures. Enterprise intelligence has emerged as a strategic capability that integrates Artificial Intelligence (AI), Data Engineering, Cybersecurity, and Intelligent Automation to facilitate informed decision-making, operational efficiency, and sustainable innovation. As organizations generate unprecedented volumes of structured and unstructured data, traditional business intelligence frameworks often fail to provide the scalability, agility, and real-time analytical capabilities required in highly competitive environments. Consequently, enterprises are increasingly investing in integrated intelligence ecosystems capable of processing large-scale datasets, ensuring data quality, protecting sensitive information, and automating complex workflows. This research investigates the role of scalable enterprise intelligence frameworks in enabling digital transformation across contemporary organizations. The study examines how AI-driven analytics, advanced data engineering infrastructures, cybersecurity mechanisms, and automation technologies collectively contribute to organizational resilience, productivity, and innovation. A conceptual research framework is proposed to illustrate the interrelationship among these technological components and their impact on enterprise performance. The research adopts a qualitative and conceptual methodology based on extensive literature analysis, industry reports, and contemporary enterprise transformation models. The findings indicate that organizations achieving successful digital transformation are those capable of integrating intelligent analytics with secure and scalable data infrastructures. Furthermore, automation technologies significantly reduce operational complexity while improving responsiveness and business continuity. The study identifies major implementation challenges, including data governance issues, cybersecurity vulnerabilities, scalability constraints, and workforce adaptation requirements. The proposed framework offers valuable insights for researchers, practitioners, and policymakers seeking to design next-generation enterprise intelligence systems capable of supporting long-term digital transformation initiatives. The study concludes that the convergence of AI, data engineering, security, and automation represents a foundational pillar for future enterprise competitiveness and innovation.
Smart VKYC with Deepfake and Liveness Detection
The fast growth of virtual banking has multiplied the demand for at ease, remote, and seamless patron onboarding, making Video-primarily based KYC (vKYC) a essential procedure. This painting provides an incorporated AI-pushed verification device called clever vKYC, designed to automate identification validation and save you rising fraud tries in actual time. The goal of the system is to bolster virtual accept as true with by means of combining multiple gadget mastering and computer-imaginative and prescient modules into an stop-to-cease verification pipeline. The methodology includes FastAPI for high-performance backend processing, Agora for actual-time video streaming, Dlib’s 68-factor landmark model for blink-primarily based liveness detection, and a transformer-based totally deepfake detection version to perceive synthetic media. additionally, a twin-layer document validation method is implemented, in which Tesseract OCR extracts Aadhaar information and Pyzbar verifies cryptographically signed UIDAI QR codes to discover tampered or forged documents. Experimental consequences display a full-size improvement in preventing identity spoofing when FaceNet512 embeddings, OCR-QR consistency assessments, and deepfake ratings are mutually evaluated. The decision engine integrates output from these modules to generate a reliable confidence score, ensuring accurate verification without compromising person enjoy. This look at demonstrates that a multi-component vKYC architecture can considerably enhance the security and efficiency of far off consumer onboarding systems.
Transforming Software Quality Assurance through Artificial Intelligence, Automated Testing, and Intelligent Software Analytics
Software Quality Assurance (SQA) plays a vital role in ensuring the reliability and performance of modern software systems. However, the increasing complexity of software applications, rapid development cycles, and adoption of technologies such as CI/CD, cloud computing, microservices, and distributed systems have created new challenges for traditional quality assurance approaches. Manual testing and conventional automation techniques are often unable to meet the demands of these dynamic environments. As a result, Artificial Intelligence (AI), Automated Testing, and Intelligent Software Analytics have emerged as effective solutions for improving software quality and testing efficiency. This study explores the integration of AI-driven techniques into software quality assurance processes and examines their impact on testing effectiveness, defect detection, and overall software reliability. The proposed framework combines machine learning-based defect prediction, automated test execution, and intelligent analytics to support continuous quality monitoring and informed decision-making. By analyzing software development data such as source code, defect records, user feedback, and runtime information, the framework helps identify potential risks and optimize testing activities. The findings indicate that AI-powered quality assurance can significantly improve defect detection rates, increase test coverage, reduce testing time, and lower maintenance costs. Furthermore, intelligent analytics enables organizations to identify quality issues at an early stage and take preventive actions before software release. The study demonstrates that integrating AI, automated testing, and software analytics can create a more efficient and reliable quality assurance ecosystem, supporting faster software delivery while maintaining high quality standards. Future research may focus on explainable AI, autonomous quality engineering systems, and deeper integration with DevSecOps practices.
Machine Learning-Enabled Self-Healing Data Pipelines: An Autonomous Architecture for Failure Detection, Diagnostic Reasoning, and Automated Remediation
Enterprise data pipelines have become essential infrastructure for analytics, artificial intelligence, risk management, digital services, and regulatory reporting. However, their reliability is challenged by issues such as schema drift, source delays, data corruption, orchestration failures, resource constraints, model drift, and downstream inconsistencies. This paper proposes an autonomous architecture for machine learning-enabled self-healing data pipelines that integrates multimodal observability, anomaly detection, diagnostic reasoning, and policy-driven automated remediation. The architecture views data pipelines as continuously monitored socio-technical systems represented through metrics, logs, traces, lineage graphs, data contracts, quality indicators, execution histories, and incident records. The proposed framework consists of five layers: (1) a telemetry and lineage substrate, (2) a feature and knowledge representation layer, (3) a detection layer combining statistical, rule-based, and machine learning techniques, (4) a diagnostic reasoning layer using causal and graph-based methods, and (5) a remediation layer that executes controlled repair actions under governance policies. The study further introduces a fault taxonomy, a remediation safety model, and an evaluation framework based on fault injection experiments. Performance is assessed using metrics such as detection precision, diagnosis accuracy, mean time to recovery, false remediation rate, data quality preservation, and auditability. The paper argues that self-healing should follow a constrained autonomy model, where automated actions are permitted only when supported by sufficient evidence, limited operational risk, validated rollback mechanisms, and explicit policy approval. By integrating AIOps, DataOps, MLOps, and software governance practices, the proposed architecture provides a research-driven blueprint for building resilient data platforms capable of early failure detection, systematic root-cause analysis, automated remediation, and evidence-based escalation.
Angular Progressive Web Application Architecture for Enterprise Financial Dashboards: Performance and Accessibility
Enterprise financial dashboards present demanding requirements for Progressive Web Applications built with Angular, combining large dataset visualization, real-time data refresh, role-differentiated views, and strict accessibility compliance for regulated financial environments. This paper examines Angular PWA architectural patterns optimized for enterprise financial dashboard delivery.
Enterprise Data Transformation Strategies with Talend and Snowflake Cloud Platform
The increasing volume and complexity of enterprise data have accelerated the adoption of cloud-native platforms and intelligent data integration solutions to support modern business operations. As enterprises deal with a growing volume and complexity of enterprise data, cloud-native platforms and intelligent data integration solutions are accelerating enterprise operations in today's modern world. The scalability, data quality, processing speed, and integration of data from multiple sources are often problematic in the traditional data management approaches. This paper outlines an enterprise data transformation framework that leverages Talend Data Fabric and the Snowflake Cloud Data Warehouse to create a scalable, secure and high performance data ecosystem. Talend is the single integration and orchestration point to efficiently collect, cleanses, transform and manage data from several enterprise systems, as well as metadata. Snowflake builds on this with its cloud-native design which separates storage and compute resources for elastic scaling and optimized analytical performance. The proposed schema follows current ELT (Extract, Load, and Transform) principles, with Snowflake's processing engine handling transformations in the cloud environment. This way, there is less infrastructure overhead, low processing latency and the ability to run real-time and near-real-time analytics. The study also underscores the need for data governance, security, data lineage and automated data quality validation to provide trust and compliance of enterprise data assets. Performance evaluation shows that the transformations are significantly faster, query execution is faster, it's much more scalable and cost efficient than traditional ETL architectures. The results show that Talend and Snowflake offer organizations a flexible, future-proof data platform with the ability to handle their business intelligence, advanced analytics, AI, and large-scale digital transformation projects.
Multi-Horizon Demand Forecasting for E-Commerce Fulfillment Using Ensemble Deep Learning
Efficient inventory management and assignment of fulfillment capacity, along with labor planning is built on accurate multi-horizon demand forecasting in large-scale e-commerce businesses. This paper introduces an ensemble deep learning approach fusing Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCN) and Gradient Boosted Trees (GBT) to predict the demand in various categories with different seasonality patterns, demand volatility, promotional effects, and customer purchasing behaviors across multiple horizons. Traditional statistical forecasting methods like Moving Average, ARIMA, and Exponential Smoothing may not effectively model the nonlinear dependencies, rapid changes in demand, and intricate temporal dynamics often seen in today's e-commerce landscape. Advanced AI and deep learning techniques have thus become a promising alternative to enhance the accuracy of forecasts. Proposed framework combines the strength of LSTM network to model the sequence, TCN for extracting long-range temporal dependencies and GCs for feature engineering. The framework achieves this by combining prediction results from these different learners using the technique of weighted ensemble aggregation, which results in increased robustness and generalization for short-term, medium-term, and long-term forecasting. It uses transactional sales data, promo signals, the holidays, customer engagement data, and stock data to provide one-day to ninety-day outlooks. A detailed experimental analysis was performed with representative e-commerce demand datasets with multiple product categories. Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Weighted Absolute Percentage Error (WAPE) were used to evaluate forecasting performance. The experimental results show that ensemble model is always superior than the single forecasting model, and it can decrease the error of the forecast and ensure the stability of the forecasting results in the case of fluctuation demand. The ensemble framework proved to be successful in enhancing the reliability of the forecasts, especially during holidays and promotional periods. The results show that hybrid deep learning-based systems can offer significant advantages for inventory management, warehouse resource management, transportation scheduling, and supply chain decision-making. In addition, multi-horizon forecasting can help companies effectively plan stock replenishment strategies, prevent stockouts, lower over-stocking expenses, and enhance customer satisfaction. The proposed methodology adds to the existing research on intelligent supply chain analytics by offering a scalable, adaptive, and data-driven approach for prediction in the dynamic e-commerce environments. Transformer architectures, reinforcement learning, and real-time demand sensing mechanisms could be combined in future upgrades to further boost forecasting accuracy and operational responsiveness.
Enterprise Android Device Management and Security Policy Enforcement for Large-Scale Retail IoT Deployments
Large-scale retail IoT deployments comprising fleets of Android-powered handheld devices, kiosks, and point-of-sale terminals require enterprise device management architectures that enforce security policies, automate software lifecycle management, and maintain operational continuity across geographically distributed store environments. This paper presents an enterprise Android device management framework for retail IoT deployments, examining Mobile Device Management protocol integration, automated application deployment pipelines, remote security policy enforcement including certificate management and encryption compliance, and zero-touch provisioning workflows for new device onboarding.
Entity Framework Core Performance Optimization Strategies for High-Throughput Financial Data Systems
Entity Framework Core is widely adopted for data access in .NET enterprise applications, yet its default behaviors introduce performance bottlenecks that become critical in high-throughput financial data systems processing large transaction volumes. This paper systematically evaluates Entity Framework Core performance optimization strategies including query compilation caching, no-tracking queries, explicit loading versus eager loading tradeoffs, bulk operation handling, and connection pooling configurations in financial services contexts. Benchmarks conducted on representative financial transaction datasets quantify the performance impact of each strategy and identify optimization combinations that yield the greatest throughput improvements. The paper further analyzes when Dapper should supplement or replace Entity Framework Core for performance-sensitive financial data access paths.
Medical Device Development with Automated DevOps Environments and Regulatory Compliance
The demand for reliable, safe, and compliant software-driven medical device development environments has increased with the changing dynamics in the field of healthcare technologies. It also has the need for patient safety and software reliability. The typical medical device software development process may involve a number of problems, including multiple testing environments, slow deployment cycles, lack of visibility, and compliance issues. A medical monitoring framework was established to overcome these challenges, which allowed for the automation of all aspects of DevOps, such as continuous integration, continuous deployment, medical services containerization, automated testing, vulnerability assessment, real-time monitoring and compliance auditing. It utilises some of the most important technologies, including Docker, Prometheus, Grafana, GitHub Actions, and cloud-native deployment methods, to improve the efficiency of software delivery and monitoring. Results of the experiment showed effective utilization of the infrastructure with an average of 1.99% CPU consumption, 376.2 MB memory consumption and application programming interface (API) response time of 1.9–3.1 ms for eight containers deployed. The system alerted 220 alerts and resolved 111 alerts (alert resolution rate = 50.5%) for continuous patient monitoring and clinical intervention. Implementation Mapping with recognised medical and cybersecurity standards provided an opportunity for regulatory alignment. Improvements in automation and observability have been achieved, but further work is needed to address issues of interoperability and more widespread clinical validation.
Cloud and Edge-Based Distributed Computing: A Survey of Architectures and Resource Management Approaches
The swift development of data-intensive applications and Internet of Things (IoT) systems has increased the rate of change in the distributed computing paradigms, especially cloud and edge computing. Although cloud computing provides scalable and on-demand services with centralized infrastructures, it has limitations in terms of high latency, bandwidth, and supporting real-time applications. To address these challenges, edge and fog computing have become complementary paradigms that can allow processing data nearer to the source, thus, decreasing the latency and enhancing responsiveness. This paper provides a wide overview of the architectures and resource management strategies in cloud and edge-based distributed computing systems. It discusses the centralized cloud architecture, edge, and fog computing models, the cloud-edge continuum that incorporates these paradigms into a single framework. Moreover, it discusses important resource management methods, including resource allocation and provisioning, task scheduling and load balancing, and QoS- and SLA-aware optimization. Data placement, communication protocols, network optimization and real-time constraints are also discussed in the paper as the data and network management strategies. Finally, it identifies the current challenges, research gaps and future directions for developing efficient, scalable and intelligent distributed computing systems.
Machine Learning-Driven Automated Quality Inspection of Steel Plates in Industrial Production Lines
Steel is a vital commodity in many different industries and the building trades. It's crucial to have high-quality steel plates to avoid any potential problems. An essential step in detecting and classifying surface defects in rolled metal is the pre-distribution quality evaluation. This study provides a CNN+BiLSTM hybrid neural network system that can automatically detect and categorize surface flaws on steel plates. The Faulty Steel Plates dataset forms basis of the technique proposed in this paper. Cleaning dataset, normalizing it using Min-Max, balancing it using SMOTE, and stratifying it are all data preprocessing procedures that are utilized to improve the dataset's quality and classification performance. In order to improve learning, CNN component records most crucial spatial features and the BiLSTM component records the sequential dependency. The performance of the proposed model is compared with traditional machine learning models such as Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). Experimental findings showed that the suggested model (CNN+BiLSTM) outperformed existing models with an F1-score (F1), recall (rec), accuracy (acc), and precision (prec) of 99.7%. The findings prove that the suggested technique is an effective and dependable way to automatically detect surface defects in steel plates in an industrial production environment.
A Study on Examining the Relationship Between Educational Technology Usage and Academic Excellence
The application of digital technology has witnessed tremendous revolution in the area of education and learning process today. This research paper focuses on Educational Technology Usage and Academic Excellence as well as the impact of Leadership Support and Educational Technology Readiness in technology-based learning process. This research is conducted using mixed research method involving descriptive and correlation studies. A total of 100 subjects (students, teachers and educational leaders) from selected educational institutions were used for the study, data collected using structured questionnaires on 5-point Likert scale. The data was statistically analyzed with IBM SPSS software. The results showed that the use of educational technology has a positive effect on academic achievement in terms of student engagement, participation in the classroom, collaboration, motivation for learning and understanding of concepts. Based on correlation analysis, there was a strong positive correlation among the variables of Educational Technology Usage and Academic Excellence. Leadership Support also showed great impact in regards to effective technology integration and the improvement of education. Moreover, the research revealed that digital engagement practices, adaptive instructional technology and collaborative digital learning are the main dimensions that facilitate technology-enhanced learning. The study finds that there is a possibility of educational technology to improve academic performance under proper infrastructure and infrastructure support, teacher readiness, institutional support and strategic leadership practices.
A Scalable AI-Driven Quality Engineering Architecture for End-To-End Validation of Core Banking, API, and UAT Ecosystems
The modernization of financial institutions has catalyzed the transition from monolithic legacy systems to highly distributed, API-centric core banking architectures. While this evolution enables unprecedented scalability and integration, it introduces profound complexities in Quality Engineering (QE). Traditional validation methodologies, particularly within User Acceptance Testing (UAT) and API integration phases, are inherently unscalable, labor-intensive, and prone to missing complex, systemic defects. This paper proposes a highly scalable, AI-driven Quality Engineering architecture designed for the end-to-end validation of core banking systems. By converging Machine Learning (ML) defect prediction models, dynamic service dependency graphs, and automated decision intelligence, the proposed framework drastically optimizes the UAT ecosystem. Empirical evaluations utilizing simulated high-frequency banking telemetry demonstrate that the integration of advanced ensemble techniques (Random Forest and Gradient Boosting) paired with graph-based test case prioritization reduces UAT cycle times by 68% while achieving a defect detection F1-score of 0.93. The architecture seamlessly integrates predictive quality assurance into the agile software lifecycle, ensuring that core banking upgrades, API deployments, and complex UAT scenarios are executed with maximum integrity, minimal automation economics overhead, and fortified cybersecurity resilience. Ultimately, this research provides a comprehensive blueprint for financial institutions seeking to achieve autonomous, predictive, and infinitely scalable quality engineering.
Autonomous Artificial Intelligence Techniques for Secure Enterprise Workflow Orchestration
The growing complexity of enterprise digital ecosystems has increased the need for intelligent workflow orchestration capable of autonomously managing operations, cybersecurity, resource allocation, and service continuity. Autonomous Artificial Intelligence (AAI) has emerged as a key technology for enabling adaptive, secure, and self-regulating enterprise workflows across hybrid cloud, edge, and distributed environments. This study examines the application of advanced AI techniques, including reinforcement learning, federated learning, deep neural networks, graph-based intelligence, explainable AI, and autonomous agents, in enterprise workflow management. The research evaluates how AI-driven orchestration improves threat detection, anomaly prediction, adaptive scheduling, compliance monitoring, and resource optimization. It also identifies limitations of traditional orchestration systems and highlights challenges related to scalability, transparency, interoperability, trust, and cybersecurity resilience. A conceptual framework integrating intelligent monitoring, predictive analytics, dynamic policy enforcement, and zero-trust security is proposed. Findings indicate that autonomous AI enhances workflow adaptability, operational efficiency, cyber resilience, and decision-making in multi-cloud enterprise environments. However, challenges related to governance, explainability, ethics, and trust remain significant areas for future research. The study provides insights into the design and implementation of secure, intelligent workflow orchestration systems and offers a roadmap for next-generation enterprise AI solutions.
Deep Learning Assisted DevSecOps Automation for High-Availability Enterprise Applications
The rapid evolution of enterprise digital ecosystems has significantly increased the complexity of software deployment, infrastructure management, and cybersecurity operations. Traditional DevOps methodologies primarily emphasize automation, agility, and continuous delivery, yet modern enterprise applications require integrated security, intelligent orchestration, and high-availability infrastructure support to withstand dynamic cyber threats and operational disruptions. In this context, Deep Learning (DL)-assisted DevSecOps automation has emerged as a transformative paradigm capable of integrating predictive analytics, intelligent anomaly detection, automated remediation, and adaptive infrastructure management into enterprise software engineering pipelines. This research article investigates the role of deep learning technologies in enhancing DevSecOps automation for high-availability enterprise applications. The study explores the integration of neural network-driven security analytics, AI-powered CI/CD pipelines, intelligent vulnerability assessment, automated threat prediction, and self-healing cloud infrastructure mechanisms. The article further examines how deep learning models improve deployment efficiency, reduce downtime, strengthen cyber resilience, and optimize operational scalability in distributed enterprise systems. Comparative analysis between traditional DevOps and AI-assisted DevSecOps frameworks demonstrates that intelligent automation significantly improves incident response time, security posture, deployment accuracy, and infrastructure reliability. The proposed framework introduces a multilayered intelligent DevSecOps architecture incorporating continuous monitoring, reinforcement learning-based orchestration, predictive maintenance, and autonomous policy enforcement. Experimental observations and industry-oriented discussions indicate that organizations adopting DL-assisted DevSecOps architectures achieve enhanced operational continuity, reduced security risks, and improved service availability. The findings contribute to the growing body of research on intelligent software engineering and AI-driven enterprise automation, offering practical insights for cloud-native organizations, cybersecurity professionals, and enterprise architects.
Data Observability Framework for Mission-Critical Industrial ETL Pipelines: Anomaly Detection, SLA Modeling, and Proactive Failure Identification
Traditional monitoring tools can confirm that an ETL pipeline has executed successfully, but they often fail to detect whether the data itself is accurate and complete. In industrial environments, pipelines may finish without errors while delivering stale, incomplete, or incorrect data, creating significant operational risks. To address this challenge, this paper presents the Industrial Observability Platform Framework (IOPF), a five-dimensional observability model comprising freshness modeling, volume anomaly detection, SLA proximity alerting, failure signature classification, and root-cause diagnostic instrumentation. The framework was implemented in a utility-sector production environment processing over 500 million rows daily across 20+ source systems and 2,000+ Oracle Data Integrator (ODI) mappings. During a 180-day evaluation period, IOPF reduced Mean Time to Resolution (MTTR) from 3–4 hours to 15–30 minutes, decreased business-reported incidents from 20–25 to 3–6 per month, and increased proactive incident detection from 10–15% to 65–80%. IOPF extends previous OT/IT integration and cloud-scale ELT frameworks by providing the observability capabilities required for reliable industrial data pipeline operations at enterprise scale.
Enterprise Data Integration Security Orchestration and Automation
Cloud Security Posture Management (CSPM)
Incident Response in Multi-Cloud Environment