International Journal of Emerging Research in Engineering and Technology
AI-Driven Billing Solutions in SaaS: Efficiency and Accuracy
In the contemporary world of software as a service SaaS management of bills is core delicate to the success or otherwise of an organization. However, most of the prior billing methods fail to meet the need for agility, speed, and accuracy that has become common in the SaaS industry due to factors such as a solid emphasis on recurring billing, tiered-based pricing, and various types of subscriptions. This paper seeks to examine the disruptive future of Artificial Intelligence (AI) in the modernization of billing systems within the SaaS industry. Automated integrated billing solutions are designed to tackle issues of inefficiency, inaccuracy, and process holdup inherent in standard billing strategies. They generate bills and implement control mechanisms against fraud, as well as analyze ideal pricing models besides observing compliance with multiple regulations. Therefore, this study aims at filling this gap by assessing the effectiveness and correctness of the AI based billing solutions for SaaS as compared to the conventional billing systems. The two billing methodologies are compared below based on their strength, such as speed, error rates, capability to integrate different pricing models and the level of satisfaction from the customers. This research makes use of questionnaire survey data obtained from the system performance of the organizations together with case study analysis on some of the most outstanding SaaS organizations. Outcomes suggest that AI system use in billing improves cycle time, costs, and errors. These systems show higher flexibility, improving the management of sophisticated subscription plans and rate tiers significantly decreasing revenue loss. Furthermore, the approach based on artificial intelligence enhances the ability to identify the fraudulent activity of users as well as ensures the integration of the presented services with accounting systems where they can perform the analysis of financial processes in real time. This paper highlighted how the integration of AI can make a significant difference when it comes to billing accuracy, operational increase, and eventual customer satisfaction within the SaaS industry.
A Scalable Microservices Architecture for Event-Based Sanctions Screening Systems
Banking organizations as well as other financial firms are increasingly requiring sanctions screening solutions as they attempt to ensure adherence to ever-changing global regulations as they process massive volumes of transactions in real time. Modern demands on conventional monolithic screening platforms are hard to satisfy because they lack scalability, are slow to process, and have high coupling between parts and low fault tolerance, resulting in inefficiencies and slower compliance decisions. This paper outlines a scalable microservices architecture for event-based sanctions screening systems, built using “cloud-native” design approaches, using asynch event-based communication, and distributed processing to enhance system responsiveness and resilience. The proposed architecture consists of core compliance functions (transaction ingestion, watchlist management, screening, match evaluation, alert generation, case management, audit logging, reporting), as distinct entities which can be deployed independently and use an event streaming platform to connect them. The research methodology involves analyzing the architectural structure, designing microservices, creating a workflow model, and studying the responses to various events and evaluating performance as the number of transactions increases, thereby measuring the services' scalability, availability, and operational effectiveness. The framework includes container orchestration, horizontal auto-scaling, API gateway management, centralized monitoring and resilient messaging, and is designed to facilitate continuous operations and regulatory compliance. Experimental metrics show significant gains in throughput, response times, fault isolation and resource utilization over the traditional monolithic approach with minimal loss of processing power during times of peak transactions. The findings highlight the compelling advantages of event handling microservices in making sanctions screening platforms more scalable, maintainable, and reliable, offering financial institutions a flexible and future-proof framework for real-time compliance, swift regulatory changes, and enterprise-level digital transformation.
Streaming Feature Stores and Real-Time ML Inference on Cloud-Native Infrastructure
In the present day's fast-changing digital world, actual time machine learning (ML) is essential for making smart, responsive apps like fraud detection, recommendation systems, dynamic pricing & personalized user experiences. The feature store is the most important part of this change. It makes sure that ML models get the information that is timely, consistent & of high quality. This study investigates the evolution of feature stores from traditional batch pipelines to modern streaming architectures that provide real-time inference. As businesses increasingly embrace low-latency decision-making, the need for streaming feature storage has surged, facilitating the rapid production, modification, and transmission of features within milliseconds. We look at how cloud-native technology, such as Kubernetes, serverless platforms, and event-driven architectures, makes it possible to build real-time machine learning systems that can handle a lot of data, are reliable, and don't cost a lot of money. We want to provide a complete picture of how streaming feature stores function in practice, the architectural frameworks that support them, and the trade-offs that come with them. We demonstrate, via a practical case study, the use of a cloud-native stack to develop a real-time machine learning pipeline capable of ingesting and transforming substantial streaming data for low-latency inference. The case study clarifies the challenges faced, the implemented solutions, and the key performance outcomes. This article offers insights & practical guidance for data & ML engineers seeking to improve their machine learning workflows with streaming their capabilities, illustrating how the amalgamation of streaming data processing, feature engineering & cloud-native principles can enable the creation of sophisticated, actual time applications.
Intelligent ETL Orchestration with Reinforcement Learning and Bayesian Optimization
As more apps that need a lot of data and real-time analytics come out, the old ways of extracting, transforming, and loading data (ETL) are becoming less useful. You can't adjust the settings, set up tasks, or even know what's going on with them. This essay discusses smart ETL orchestration, a flexible method that leverages Reinforcement Learning (RL) and Bayesian Optimization to adapt how data pipelines work. Companies need smarter orchestration when they have to deal with data that changes, processing needs that change, and system resources that change. Reinforcement Learning lets ETL make better decisions on the fly by letting pipelines learn from feedback, change to new workloads, and improve scheduling in real time. Bayesian Optimization also makes it easy to adjust variables like the size of a batch, the work schedule, and how resources are split up. This is because it looks at how likely each outcome is. This is a better method to save money and get better performance from systems that are cloud-native. These strategies work together to get rid of manual calibration and replace it with learning and improvement that never stops. The essay uses a mid-sized financial services organization as an example to explain how this hybrid orchestration strategy is put together and how it operates. The deployment decreased cloud expenses by up to 23%, made tasks less likely to fail, and enhanced throughput. There are a lot of important aspects to this system. For example, there is a modular orchestration system that uses both Bayesian search methods and agents that learn through reinforcement. There is also a system of rewards that pays people for meeting their service level agreements and using resources wisely. Lastly, there is a decision layer that decides the optimal methods to do things during the ETL processes. This method shows you how to construct ETL pipelines that can change and get better on their own, step by step. It also shows how hard it is to work with real-world data. This study indicates that technology can be leveraged to provide smart orchestration. It also talks about how it could help businesses, like how it allows teams to move from set schedules to data operations that are more flexible, effective, and reliable.
Cloud-Native ETL Automation: Leveraging AI/ML to Build Resilient, Self-Healing Data Pipelines
In the present day's fast-changing, data-driven world, traditional ETL (Extract, Transform, Load) techniques typically don't work well with the size, speed & the complexity of modern data ecosystems. This paper looks at how these cloud-native architectures, together with AI and ML, are changing ETL automation to create data pipelines that are more resilient, scalable, and able to fix themselves. Moving ETL processes to the cloud lets businesses shift away from rigid, monolithic infrastructures & adopt a modular, event-driven approach that changes with the information. The main idea behind this improvement is self-healing pipelines, which are systems that can find more issues, predict them, and fix them without any help from these people. The research looks at how AI and ML models may be used for things like finding more anomalies, adapting transformation logic, optimizing workloads, and fixing bugs intelligently. We focus on important patterns and technologies in these cloud-native ecosystems, including container orchestration, serverless computing, and the event streaming platforms like Apache Kafka. Together, they provide for a very responsive and cost-effective pipeline design. We show how engineers may increase their operational resilience by using cloud-native components and AI-driven observability using actual world design ideas and the architectural patterns. We look at how these technologies greatly reduce the amount of human monitoring and maintenance work that has to be done, freeing up engineering resources and speeding up the time it takes for business users to get insights. This study argues for updating ETL processes using a self-healing, AI/ML-based method that finds more problems before they happen, makes changes in the actual time, and makes sure that data is too reliable even while things are changing. This shift in the way things work will not only lead to little improvements, but also a big step forward in how these organizations manage, control, and trust their data pipelines.
Ethical and Regulatory Challenges in Cybersecurity: A Survey of Data Governance and Policy Mechanisms
The rapid growth of intelligent systems has transformed the way organizations generate, manage, and secure data. While these technologies enable improved operational efficiency and data-driven decision-making, they also introduce significant challenges related to data governance, privacy, security, ethical AI, and regulatory compliance. Effective data governance and policy mechanisms are essential for ensuring data quality, integrity, accountability, and resilience in increasingly complex digital environments. This paper presents a comprehensive review of cybersecurity, data governance frameworks, policy enforcement mechanisms, and the ethical and regulatory challenges associated with managing cybersecurity data. It examines key governance principles, AI-driven governance approaches, privacy-preserving techniques, and regulatory frameworks such as the General Data Protection Regulation (GDPR) and the Nigeria Data Protection Regulation (NDPR). Furthermore, the paper provides a comparative analysis of recent studies on data governance and policy mechanisms, highlighting their objectives, contributions, limitations, and future research directions. Based on the identified research gaps, the study emphasizes the need for adaptive, intelligent, and automated governance frameworks capable of supporting real-time policy enforcement, risk assessment, explainable AI, and compliance management across cloud, IoT, and multi-tenant environments. The findings offer valuable insights for researchers, practitioners, and policymakers seeking to develop secure, trustworthy, and ethically responsible data governance solutions for next-generation cybersecurity systems.
Accelerating AI at Scale: The Strategic Role of HBM and Advanced Memory Architectures in Next-Generation SoC Design
Artificial intelligence (AI) has been the driving force behind digital transformation of multiple industries as it has brought in sophisticated features like generative AI, autonomous systems, large language models, and real-time data analytics. Although AI models have been consistently advancing in their scale and level of detail, it turns out that the latest System-on-Chip (SoC) architectures are not able to take full advantage of computational power but rather memory bandwidth and data movement inefficiencies issues. A widening gap between processor speed and memory operations has led to the most critical constraint in computer systems resulting in an increase in latency, power consumption, and finally a decrease in overall system efficiency. Under such circumstances, efficient data movement has become a major focus for AI acceleration at scale to be sustained. High Bandwidth Memory (HBM) is well known along with advanced memory hierarchies that combine on-chip caches, near-memory computing techniques, and heterogeneous memory architectures as leading candidates to overcome the above difficulties. Through this paper, we investigate the key function of HBM and advanced memory architectures in driving performance, scalability, and energy efficiency for next-generation AI-centric SoC designs. The research method integrates architectural study, performance appraisal, and a comparative case study of AI workloads on regular memory subsystems versus HBM-based platforms. The performance measurement criteria that include bandwidth utilization, latency reduction, throughput enhancement, and power efficiency have been reviewed to evaluate the impact of advanced memory incorporation. Results indicate that HBM has an impressive capacity to alleviate memory access bottlenecks, raise data throughput levels, and support the efficient execution of data-heavy AI models. Additionally, the paper emphasises on the benefits brought by smart memory hierarchy configuration in maximizing resource usage and catering to future AI tasks that are likely to require extremely high levels of computing and memory performance. Besides enabling a deep grasp of AI acceleration based on memory, the paper equips semiconductor designers, system architects, and other researchers, practically involved in the development of fast and scalable SoCs for the upcoming wave of AI applications, with the necessary insight.
Building Intelligent Enterprise Platforms: Integrating Big Data, AI, and Cloud-Native Architectures
Digital transformation has become a strategic focus for modern organizations looking to improve competitiveness, operational efficiency and creativity through technology-driven solutions. In this setting, data-driven decision making has become a vital competency for enterprises to extract actionable insights from large and heterogeneous data sets. The rapid growth of Artificial Intelligence (AI), Big Data Analytics and Cloud-Native Architectures further accelerates the evolution of intelligent enterprise platforms capable of dealing with real-time analytics, automation, scalability and adaptable business processes. The present study is devoted to the investigation of the integration of these technologies and presents a comprehensive framework for the development of intelligent enterprise platforms based on cloud-native infrastructures, big data ecosystems, and AI-powered decision support systems. The report outlines the core architectural elements, integration approaches and implementation considerations for delivering agility, resilience and intelligent automation throughout the company. The results reveal that the combined use of AI, big data, and cloud-native technologies has a substantial effect on operational performance, prediction, resource efficiency, and business agility. The report provides a practical reference model for enterprises to drive their digital transformation efforts, while overcoming difficulties such as scalability, data governance, security and interoperability. The practical consequences emphasize how organizations can upgrade legacy systems, promote innovation, and build durable competitive advantages through the deployment of intelligent platforms. Future research should focus on developing areas such as edge intelligence, federated learning, explainable AI, autonomous operations, and sustainable cloud computing for further improving the effectiveness and trustworthiness of next-generation corporate platforms.
Intelligent Software Automation Platforms for Sophisticated Quality Engineering Applications
In modern software applications, especially complex and evolving ones, intelligent automation frameworks for QA processes play a crucial role. Static Test Cases and Rule-Based Automation are insufficient in terms of scalability, flexibility and defect prediction in CI/CD centric environments. In this paper, a new intelligent framework for automating the end-to-end QA process using machine learning techniques, autonomous agents and feedback optimization is introduced. The frame7work will have adaptive components which will allow the system to learn and become better at testing through telemetry, test results and previously occurred defects patterns. Also, it will provide orchestration abilities for distributed testing and dynamic high risk component identification in complex software systems. This paper introduces three major contributions: First, an adaptive test case generation technique based on intelligent monitoring of system's behavior, Second, defect prediction module based on reinforcement learning to dynamically update the risk profile, Third, a decentralized multi-agent collaboration framework for effective test automation. The experiments show clear improvement in percentage of detected defects; reduced cost of maintenance and shorter release cycle compared to traditional automation frameworks.
Artificial Intelligence-Based Software Automation
Artificial Intelligence-Enabled Test Optimization
Physics-Guided Machine Learning for Rapid Nonlinear Seismic Safety Assessment of Reinforced-Concrete Frame–Wall Buildings
Purpose: This study develops a machine-learning-assisted framework for rapid seismic safety assessment of reinforced-concrete frame-wall buildings using nonlinear seismic analysis outputs. The framework is intended to reduce the time required for preliminary post-earthquake structural evaluation while retaining key indicators of nonlinear building response, including inter-storey drift, roof displacement, plastic-hinge development, and seismic performance state. Methodology: A detailed reinforced-concrete building model is developed in ETABS using available structural geometry, material properties, member sections, loading conditions, diaphragm assignments, and seismic parameters. Nonlinear static pushover and nonlinear response-history analyses are used to generate seismic-response data under systematically varied structural and ground-motion conditions. Structural properties, elastic-response indicators, modal characteristics, and earthquake-intensity measures are used as input variables for machine-learning models. Regression models predict continuous seismic-demand parameters, while classification models identify building safety states. Model performance is evaluated using grouped validation procedures, prediction-error measures, safety-classification metrics, and explainable feature-importance analysis. Findings: The study is expected to show that machine-learning models trained on nonlinear seismic simulations can estimate key response measures and classify seismic safety conditions with substantially lower computational time than repeated nonlinear analyses. The evaluation will identify the most reliable model for drift and safety-state prediction, quantify false-safe classification risk, and determine the structural and seismic variables that most strongly influence predicted performance. Unique Contribution to Theory, Practice and Policy: The study contributes a physics-guided, data-driven seismic assessment approach that links detailed nonlinear structural analysis with rapid predictive modelling for reinforced-concrete buildings. It provides a practical basis for prioritising detailed inspections, emergency-response decisions, and post-earthquake safety screening. The framework should be applied as a decision-support tool alongside professional engineering judgement, particularly where predicted risk or model uncertainty is high.
Performance-Based Earthquake Engineering
An Explainable Generative AI Framework for Insurance Claims Intelligence and Risk Assessment
Insurance claims are becoming more complex, necessitating more sophisticated, clear, and automated risk evaluation processes. This research proposes a framework for the analysis of insurance claims and human decision support that combines the use of ML, explainable AI (XAI), and generative AI. The Insurance Claims and Policy Data dataset has 53,503 records, and these have been preprocessed using data cleaning, feature engineering, label encoding, and feature scaling of the data in a public repository. Several baseline models, namely Extra Trees Classifier (ETC), Gradient Boosting Classifier (GBC) and Multi-Layer Perceptron (MLP), are built and fused using a Hybrid Soft Voting Ensemble. To assess the proposed framework, the following metrics are used: Accuracy, Balanced Accuracy, Precision, Recall, Weighted F1-Score, Cohen's Kappa, MCC and ROC-AUC. The experimental findings show that compared to the individual models, the hybrid ensemble model has the best classification accuracy of 100%. Moreover, SHAP-based explainability helped to reveal the most critical features to capturing insurance risk prediction and Generative AI facilitated what-if risk scenario simulation, real-time claims intelligence, settlement estimation and automated decision support. The envisioned framework offers accurate, robust, and interpretable solution for smart insurance claims handling and risk assessment.
Explainable Artificial Intelligence (XAI)
A Governance Framework for Application Programming Interface Lifecycle Management in Large-Scale Enterprise Digital Transformation
Application Programming Interface (API) governance has become a key part of enterprise digital transformation, helping organisations manage the entire lifecycle of APIs from creation and deployment to versioning, deprecation, and eventual shutdown through clear and enforceable policies. Companies that work in multi-vendor, hybrid-cloud environments often face ongoing issues like API sprawl, inconsistent security practices, fragmented cataloguing, and the lack of consistent deprecation strategies that work across the whole organisation. A well-structured governance model helps by setting up centralised control, standard design rules, automated enforcement tools, and stage-based lifecycle management, which lowers operational risks and improves the experience for developers. Proper API cataloguing makes sure that every active interface has clear ownership, specification details, and metadata about how it is used, forming a central record that supports all future governance decisions. Versioning policies help manage multiple versions of an API during change periods, keeping older versions functional while guiding users to newer, safer versions through clear depreciation schedules and migration plans. Security measures, applied consistently to all active and outdated endpoints using API gateway controls and integration with identity and access management systems, help eliminate security risks from unmonitored legacy interfaces. In large enterprises, governance must balance overall standards with the ability of different teams to work independently, often using federated models that keep product teams aligned with company goals without limiting innovation. Automation, built into continuous integration and delivery processes through policy-as-code and automated checks, turns governance from a one-time compliance check into an ongoing, automatic control process. Combining cataloguing, versioning, deprecation planning, and security enforcement leads to better compliance, faster development, and easier access to reusable assets across the entire enterprise API landscape.
Machine Learning Based Clinical Trial Performance Prediction for Medical Industry Applications
Clinical trials are essential for evaluating the safety and effectiveness of medical treatments; however, they often involve high costs, long durations, and uncertain outcomes. This study investigates the possibility of predicting clinical trial results using machine learning (ML) techniques by analyzing patients’ intraoperative and postoperative adverse reactions, postoperative vital signs, and satisfaction levels in trials of new sedative drugs. A Clinical Trials dataset comprising 13,748 records and 11 attributes was utilized, followed by comprehensive preprocessing steps including missing value handling, label encoding, feature scaling, and data balancing using SMOTE to address class imbalance. Machine learning models including Logistic Regression (LR), Random Forest (RF), Convolutional Neural Networks (CNN), and the proposed Extreme Gradient Boosting (XGBoost) were implemented for comparative analysis. Experimental results demonstrate that XGBoost outperforms all other models with an accuracy of 92.7%, precision of 95.6%, recall of 95.9%, and F1-score of 95.7%, indicating superior predictive capability and robustness. The comparative analysis confirms that ensemble-based learning methods, particularly XGBoost, are highly effective for clinical trial outcome prediction, offering improved reliability and decision-support potential in medical research.
Enterprise AI Copilot Architecture for CRM Systems: Human-AI Collaboration for Sales Intelligence, Customer Operations, and Decision Support
Customer relationship management (CRM) systems have evolved from operational repositories into enterprise decision platforms that mediate sales execution, customer operations, service personalization, and managerial decision support. The emergence of generative artificial intelligence, retrieval-augmented generation, large language models, and conversational copilots creates a new architectural opportunity: CRM systems can be transformed into human-AI collaborative environments where sales representatives, service agents, managers, and operations teams work with intelligent assistants that retrieve contextual knowledge, generate recommendations, summarize customer histories, automate routine actions, and support complex decisions. However, current CRM automation often remains fragmented across predictive scoring, workflow automation, dashboards, and isolated conversational bots. These systems frequently lack explainability, governance, role-aware access control, decision traceability, and reliable integration with enterprise knowledge. This paper proposes an enterprise AI copilot architecture for CRM systems that integrates human-AI collaboration, sales intelligence, customer operations, and decision support within a governance-centered, context-aware, and explainable framework. The proposed architecture combines CRM data integration, retrieval-augmented knowledge grounding, role-specific copilot orchestration, explainable recommendation services, human-in-the-loop validation, audit logging, and continuous learning. The paper contributes a conceptual model, methodological framework, evaluation criteria, and analytical discussion for deploying AI copilots in enterprise CRM environments. It argues that effective CRM copilots should not be designed as autonomous replacements for human judgment but as socio-technical decision partners that enhance customer understanding, operational responsiveness, and strategic alignment while preserving accountability, trust, and compliance.
A Hybrid Machine Learning and Control-Theoretic Framework for Stability-Assured Resource Management in Large-Scale Cloud Computing Environments
Large-scale cloud computing environments must continuously allocate, scale, and reconfigure resources under uncertain demand, multi-tenant interference, heterogeneous infrastructure, and stringent service-level objectives. Conventional threshold-based autoscaling remains widely used because of its operational simplicity, yet it often reacts after performance degradation has already occurred. Purely machine-learning-driven methods can improve prediction and adaptation, but they may produce unsafe actions when exposed to distribution shifts, delayed actuation, noisy telemetry, or unobserved dependencies. This paper proposes a hybrid machine learning and control-theoretic framework for stability-assured resource management in large-scale cloud computing environm ents. The framework integrates workload forecasting, online quality-of-service modeling, constrained optimization, feedback control, Lyapunov-style stability reasoning, and policy-governed decision intelligence. The proposed design separates predictive intelligence from safety-critical actuation: machine learning estimates near-future demand, performance sensitivity, and workload classes, while a constrained model-predictive controller and supervisory stability guard transform those estimates into resource actions that respect service-level, cost, and stability constraints. The framework is formulated for containerized and virtualized cloud platforms, including horizontal scaling, vertical resource adjustment, admission control, and workload placement. It defines a conceptual architecture, analytical stability conditions, evaluation metrics, and deployment implications for cloud operators. The analytical discussion shows that a hybrid design can reduce elastic lag, control oscillatory scaling behavior, preserve bounded latency error, and support auditable resource governance more effectively than purely reactive autoscaling or unconstrained learning policies. The paper contributes a structured research model for stability-aware cloud resource management and identifies future directions in safe reinforcement learning, distributed control, explainable autoscaling, and production-grade validation.
Cloud Data Engineering Strategies for Large-Scale Financial Data Integration and Intelligent Corporate Performance Reporting
Despite the presence of powerful ERP, CRM, banking, regulatory, market, etc. applications, enterprise financial data is increasing rapidly making it difficult for organizations to get timely, accurate and importantly, actionable corporate financial insights from the data. The coming age of high volumes, velocities, and varieties of financial information can overwhelm traditional data integration and reporting architectures, causing data silos, reports with delays, governance issues, and poor analytical power. This paper introduces a comprehensive cloud data engineering framework to enable efficient integration of structured and unstructured financial data at scale into a cloud-native architecture coupled with automated ETL/ELT pipelines, data lakehouse architecture, and a real-time analytics platform that can deliver intelligent, corporate performance reports. The strategy proposed combines heterogeneous financial data to create a unified data environment, ensuring data quality, security, compliance, and scalability. Moreover, an intelligent reporting solution is presented based on sophisticated techniques in advanced analytics, machine learning and business intelligence technologies that automates performance monitoring, financial forecasts, anomaly detection and executive decision making. Experimental evaluation and enterprise deployment analysis show that the resulting architectures offer significant gains on data processing, reporting latency, scalability, and decision making effectiveness over the existing reporting architectures. The study proposes a complete model for an architectural solution that can integrate concepts of cloud data engineering and financial aspects with the use of intelligent analysis. The results show that companies that make the switch to cloud-native financial data platforms can benefit from more transparency in operations, quicker reporting cycles, better regulatory compliance, and better strategic planning. The framework will be a key reference for companies looking to embark on digital transformation projects, and as AI evolves, it will form the basis for more sophisticated financial intelligence systems and automated corporate performance management tools.
A Unified Framework for Secure Data Platforms: Combining Data Engineering, AI Analytics, and Intelligent Threat Detection
The exponential growth of enterprise data has transformed organizational decision-making processes, creating unprecedented opportunities for innovation, operational efficiency, and competitive advantage. Modern enterprises rely heavily on secure data platforms capable of collecting, processing, storing, and analyzing vast volumes of structured and unstructured data generated from diverse sources, including cloud services, Internet of Things (IoT) devices, business applications, and digital ecosystems. However, the increasing complexity of data environments has simultaneously introduced significant cybersecurity challenges, including unauthorized access, insider threats, advanced persistent attacks, ransomware, and data leakage incidents. Traditional security mechanisms often operate independently from data engineering and analytics infrastructures, resulting in fragmented protection strategies and limited situational awareness. This study proposes a Unified Framework for Secure Data Platforms that integrates data engineering, artificial intelligence (AI) analytics, and intelligent threat detection into a cohesive architecture. The framework combines scalable data pipelines, governance mechanisms, machine learning-driven analytics, and real-time security monitoring to create a resilient enterprise data ecosystem. The proposed architecture emphasizes seamless integration between data acquisition, processing, analytics, and cybersecurity layers while enabling continuous threat intelligence and adaptive risk management. A systematic review of existing literature was conducted to identify current trends, limitations, and integration challenges associated with secure data platforms. Comparative analysis revealed that most existing frameworks focus on either data management or cybersecurity, with limited emphasis on unified operational intelligence. The proposed framework addresses this gap by establishing collaborative interactions among data engineering processes, AI-based analytical capabilities, and intelligent threat detection systems. The theoretical evaluation demonstrates that the proposed architecture enhances data quality, operational visibility, predictive analytics accuracy, and cybersecurity resilience. Furthermore, the framework supports regulatory compliance, governance enforcement, and automated threat response capabilities. The findings contribute to the development of next-generation enterprise data platforms capable of supporting secure, intelligent, and scalable digital transformation initiatives.
Runtime Integration Failure Modes in Module Federation A Taxonomy and Mitigation Patterns
Module Federation, introduced with Webpack 5 in October 2020, changed how JavaScript applications share code across independently deployed units. The mechanism makes micro-frontend composition practical at scale, but it also brings in a set of runtime failures that havse no equivalent in single-bundle applications. These failures do not appear at build time. They are deferred, sometimes silent, and often depend on load order or network conditions to reproduce. This paper identifies seven categories of runtime failure in Module Federation deployments, describes what causes each one and what it looks like when it occurs, and proposes a set of practical mitigation patterns with known tradeoffs. Three architecture diagrams and three reference tables are included. The intent is to give engineering teams a shared vocabulary for the failure classes most likely to cause production incidents in federated frontend systems.
Applying 8D to Customer Complaints: Turning Negative Feedback into Process Improvement
Customer complaints are often treated as isolated quality incidents rather than opportunities for organizational learning. This paper explores the application of the Eight Disciplines (8D) problem-solving methodology – a structured, team-oriented approach—to transform negative customer feedback into sustainable process improvements. Customer complaints are often perceived as negative feedback; however, they provide valuable insights into product and process deficiencies. The Eight Disciplines (8D) problem-solving methodology is widely used in automotive and manufacturing industries to systematically address such issues. This paper also presents a structured approach to applying the 8D methodology to customer complaints, transforming them into opportunities for continuous improvement. Real-time examples, tables, and visual representations are included to demonstrate practical implementation.
A Hybrid MIP-ML Framework for Real-Time 3D Bin Packing in High-Volume E-Commerce Fulfillment
The three-dimensional (3D) bin packing problem is a well-known NP-hard combinatorial problem that is fundamental to the emerging e-commerce logistics demands of today, which require millions of packing requests to be solved every day, with high-quality service and acceptable computational effort. Traditional exact optimization methods like Mixed Integer Programming (MIP) can generate optimal packing schemes; however, for large and real-time applications, such traditional methods become hard to apply. On the other hand, heuristic or machine learning approaches have quicker decision-making capabilities and have the potential of compromising solution quality and consistency. This paper introduces a hybrid MIP-ML approach, merging the advantage of real-time optimality guarantees of MIP with the powerful predictive capability of machine learning, and proposes an implementation of it. Addressing high-volume fulfillment, this paper suggests a hybrid MIP-ML framework based on optimism of real-time MIP results and the efficiency of prediction capabilities of machine learning, and introduces an implementation. This approach is based on an optimization layer called “MIP,” which processes a number of representative problem instances to create optimal solutions, which are subsequently used to train machine learning models that allow for quick prediction of near-optimal container selection and pack configuration. A hybrid decision engine dynamically switches between exact optimization and predictive inference, depending on the constraints and computational budget. Experiments have shown that the proposed framework is not only significantly faster to decide, but also enables high packing utilization and container efficiency. Results show significant enhancements in operational scalability, packaging cost reduction, and fulfillment throughput over traditional optimization-only solution approaches. The proposed architecture offers a practical and scalable approach to implement an intelligent packaging optimization system in the next-generation e-commerce fulfillment center.
SAP S/4HANA Transformation in the Electric Power and Grid Utility Sector: Combination Migration Strategy and Customer-Managed Deployment A Practitioner's Analysis
The global electric power and grid utility sector faces a dual transformation imperative: the approaching SAP ECC 6.0 maintenance deadline of December 2027 and the accelerating demands of energy transition, grid modernization, and regulatory compliance. This paper presents a comprehensive practitioner analysis of a large-scale SAP S/4HANA transformation program executed for a major vertically integrated electric utility operating across transmission, distribution, and generation asset portfolios. The program employed a combination migration strategy, Greenfield reimplementation for the enterprise core covering Finance, Asset Management (IS-U/PM), Work Order Management, and Grid Operations, paired with brownfield system conversion for three regional subsidiary instances deployed on a customer-managed S/4HANA private cloud architecture. The analysis examines utility-specific architectural considerations, including SAP Industry Solution for Utilities (IS-U) migration, Plant Maintenance and Enterprise Asset Management (EAM) transformation, regulatory compliance, and NERC-CIP cybersecurity requirements, and grid operations integration. Program outcomes include a 61% reduction in the custom ABAP estate, a 42% improvement in asset maintenance scheduling efficiency, full IS-U migration with zero customer billing interruption, and a projected five-year total cost of ownership reduction of $16.8 million against a $44 million program baseline. The paper provides an integrated decision framework for utility enterprises navigating S/4HANA strategy selection, with dedicated attention to IS-U/S4HANA Utilities migration, EAM transformation, and regulatory compliance architecture.
Predictive Failure Intelligence for Enterprise-Scale Data Engineering: A Proactive Framework for Monitoring, Forecasting, and Preventing Data Pipeline Disruptions
Enterprise-scale data engineering has become a mission-critical capability for organizations that depend on analytics, artificial intelligence, machine learning, regulatory reporting, personalization, and real-time decisioning. Yet many production data environments still operate with reactive observability models: pipeline failures are detected after service-level agreements are breached, downstream dashboards become stale, model features drift silently, or business users escalate data quality defects. This paper proposes Predictive Failure Intelligence (PFI), a proactive framework for monitoring, forecasting, and preventing data pipeline disruptions across heterogeneous enterprise data ecosystems. The framework integrates multi-layer telemetry, data quality profiling, workload forecasting, dependency-aware failure modeling, anomaly detection, risk scoring, automated remediation, and governance controls into a unified operating model. Unlike conventional monitoring approaches that focus on component health or binary task success, PFI treats pipeline reliability as an emergent property of data semantics, infrastructure behavior, orchestration state, schema evolution, lineage, service-level objectives, and organizational response capacity. The paper develops a conceptual architecture, defines core metrics, presents an implementation methodology, and proposes an evaluation design suitable for batch, streaming, lakehouse, warehouse, and machine-learning-oriented data platforms. The central contribution is a structured failure-intelligence lifecycle that shifts data engineering operations from incident response to risk anticipation, from dashboard-centric observability to decision intelligence, and from isolated alerts to governed prevention. The paper argues that enterprise data reliability requires coupling predictive models with architecture-centered governance, because the value of early warning depends not only on model accuracy but also on the ability to explain, prioritize, and safely act on predicted risk.
AI-Assisted Requirements Engineering for Agile Software Development: A Transformer-Based Framework for Requirement Classification, Conflict Detection, and Traceability
Agile software development relies on rapid elicitation, continuous refinement, and iterative delivery of requirements, yet many teams still manage requirements through manual story grooming, spreadsheet-based trace matrices, and ad hoc quality checks. These practices are increasingly insufficient when software systems must satisfy complex functional expectations, non-functional constraints, regulatory obligations, cybersecurity controls, and architecture-dependent deployment risks. This paper proposes T-REACT, a transformer-based requirements engineering framework for agile software development that integrates requirement classification, semantic conflict detection, and automated traceability into a unified decision-support pipeline. Rather than treating requirements engineering as a document-production activity, the proposed framework models backlog items, user stories, acceptance criteria, test cases, defects, architecture decisions, and deployment risks as interconnected semantic artifacts. The framework uses contextual embeddings, fine-tuned requirement classifiers, pairwise contradiction scoring, dependency-aware graph reasoning, and human-in-the-loop governance to support sprint planning, backlog refinement, change impact analysis, and release readiness decisions. The study follows a design-science research approach and presents the framework architecture, data model, model-training workflow, evaluation protocol, agile integration pattern, and governance controls. A demonstration scenario shows how the framework identifies requirement categories, highlights likely conflicts, recommends trace links, and produces auditable explanations for product owners, business analysts, architects, testers, and compliance stakeholders. The paper contributes an end-to-end research artifact that connects transformer-based natural language understanding with agile lifecycle governance, emphasizing explainability, traceability, privacy, continuous learning, and operational adoption. The proposed framework is intended to reduce ambiguity, improve backlog quality, support earlier defect prevention, and strengthen alignment among requirements, design, testing, and release decisions.
Agentic AI for Autonomous Software Maintenance: A Retrieval-Augmented Multi-Agent Framework for Bug Localization, Patch Generation, and Regression Validation
Autonomous software maintenance is becoming a critical research direction as modern codebases grow beyond the practical reach of manual debugging, release governance, and regression validation. Large language models have demonstrated strong code-generation capabilities, but repository-level maintenance remains difficult because defects frequently arise from interactions among multiple files, hidden dependencies, runtime configurations, and incomplete issue reports. This paper proposes AutoMaint-RAG, a retrieval-augmented multi-agent framework for autonomous software maintenance. The framework coordinates specialized agents for issue interpretation, repository retrieval, fault localization, patch synthesis, static analysis, test generation, regression execution, and release-risk governance. Unlike single-agent code-generation workflows, AutoMaint-RAG treats maintenance as a controlled software-engineering process in which every generated patch must be grounded in repository evidence, ranked suspicious locations, executable test feedback, and auditable decision records. The proposed framework integrates hybrid retrieval over code, commit history, test traces, dependency graphs, and operational telemetry; combines spectrum-based fault localization with semantic ranking; and applies iterative patch refinement through execution-guided feedback. A reproducible validation protocol is defined using repository-level issue benchmarks, mutation-based regression assessment, and industrial-style CI/CD gates. The paper contributes a formal architecture, agent-interaction protocol, patch-risk scoring model, evaluation design, and governance layer for deploying agentic AI in safety-sensitive software maintenance environments. The central argument is that autonomous maintenance should not be reduced to prompt-based patch generation; rather, it requires an engineered socio-technical pipeline that constrains language-model creativity through retrieval grounding, tool execution, regression evidence, and human-review escalation. The proposed framework advances a research agenda for maintainable, auditable, and operationally reliable AI agents in software lifecycle management.
Generative AI Integration Patterns for Enterprise Workflow Automation: A Practitioner Framework
While the transformative potential of Generative Artificial Intelligence (GenAI) technologies for enterprise workflow automation is undeniable, their ability to fit into the existing business process infrastructures in production-scale contexts is not fully understood. Companies are able to leverage knowledge-intensive tasks that were otherwise rule-based with the rapid growth of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), multimodal AI systems, and autonomous agent architectures. But enterprises still have a lot of problems as far as scalability, governance, security, explainability, compliance, integration complexity and reliability go. Current research has largely concentrated on algorithmic performance and model development, and there is little focus on patterns for integrating the models in the real world in a way that enables sustainable enterprise adoption. Drawing on current use cases in the industry from sectors such as retail, e-commerce, customer support, data governance, software engineering and knowledge management, this paper proposes a practitioners-oriented framework for the integration of Generative AI in enterprise workflow automation. The framework outlines common architectural patterns that support enterprises in integrating Generative AI into their current digital ecosystems while ensuring governance, accountability, and operational efficiency. The proposed framework comprises four key layers of integration: Enterprise Data Intelligence Layer, AI Orchestration Layer, Human-in-the-Loop Governance Layer, and Workflow Execution Layer. These layers work together to enable intelligent process automation, contextual decision support, automated content creation, anomaly detection, enterprise knowledge retrieval, and adaptive business process execution. The study also breaks down GenAI models for enterprise deployments into Assistive Automation, Collaborative Intelligence, Autonomous Task Execution and Agentic Enterprise Systems. Every category is a different stage of AI adoption and readiness within the organisation. The study provides a comparative analysis of scenarios for implementation, showing how organizations can move gradually toward more autonomous and semi-autonomous operational ecosystems. Its framework also includes governance measures related to ethical AI, privacy, transparency, model monitoring and risk management. Practitioner-driven assessment criteria, including automation efficiency, the quality of the responses, operational scalability, integration complexity, governance maturity and business value realization, are applied to methodological evaluation. They show that structured integration patterns lead to process efficiency, faster decision-making, better customer experience, and enhanced business agility. In addition, the use of retrieval-augmented architectures and oversight mechanisms adds significantly to the trustworthiness and compliance results. The results offer theoretical and practical insights, filling the gap between the new capabilities of Generative AI and enterprise automation needs. The proposed framework offers organisations guidance to design actionable AI-powered workflow ecosystems that are scalable, secure and governable. The framework acts as a strategic roadmap for businesses to realize the value of Generative AI and reduce implementation risk, ensuring sustainable business outcomes as they navigate their digital transformation journey.
Redux State Management Patterns for Large-Scale React.js Enterprise Applications: An Empirical Analysis
As applications grow in size and complexity to encompass multiple enterprise departments and teams, state management issues have become a major technical debt, performance and maintainability problem in large scale React applications. Enterprise frontends systems are evolving quickly, and there is a need for scalable architectures to support large amounts of application state across distributed user interfaces and micro frontends. Its consistent state container model, centralized data flow and middleware support is what made Redux one of the most popular state management libraries for React.js applications. As the size and complexity of these enterprise applications grow, however, these organizations encounter issues with state normalization, selector optimization, orchestration with asynchronous middleware, modular slice architecture, and long-term maintainability. This paper explores an empirical study of Redux state management patterns in enterprise retail frontend systems, capturing the performance and maintainability costs of normalized state trees, selector memoization tactics, middleware patterns and slice-based modularization. The study explores the impact of various approaches towards implementing Redux on rendering speed, code maintainability, scalability, and developer productivity in enterprise-level single-page apps. The research method is a comparative experimental approach which includes several large-scale React.js applications with their respective simulated enterprise retail workflows, customer service operations and inventory management systems. Performance indicators like rendering latency, state update throughput, memory usage, component re-render frequency and maintainability indices are tested under different workloads. The empirical evaluation shows that the normalized state structures and memoized selectors can drastically reduces the number of unnecessary component updates and responsiveness of the app. Moreover, they are easy to organize and make the state architecture more modular in Reborn, along with reducing technical debt and boilerplate code by using Redux Toolkit and slice architectures. The study also delves into middleware optimisation using asynchronous processing models such as thunk based and event-driven, and how they impact on the handling of network requests and business process orchestration. The results show that optimized middleware pipelines help improve application stability and minimize state synchronization cost in distributed enterprise applications. Furthermore, this study takes advantage of the latest advancements in frontend governance, metadata-driven architectures, and intelligent development environments to establish Redux as a cornerstone technology in contemporary enterprise ecosystems. Results indicate that the application of governance principles along with the application of state management strategies have an impact on improving the quality assurance of software and can support sustainable evolution of software applications. The suggested empirical model offers valuable insights and guidance for software architects, frontend developers, and enterprise technology decision makers looking to enhance React.js application scalability and maintainability. The study advances the current knowledge of frontend software engineering, setting a full-fledged evaluation model for Redux state management patterns and finding some best practices for upcoming enterprise application building.
OAuth 2.0 Security Patterns for AI-Augmented Microservices: Challenges and Design Principles
When building enterprise microservices architectures with the advent of AI agents and automated flows, new security concerns emerge that were unforeseen by the original OAuth 2.0 framework. The paper extensively reviews security risks in service meshes used in an agentic context and explores the concepts of scope management, agentic identity verification and least privilege security in the context of services, reviewing the best practices and approaches for each of these areas. We share a design principles list that has been practically proven as suitable to safely integrate AI agent interactions into an existing OAuth 2.0 based framework within a large scale-retail enterprise. Empirical results confirm that naive OAuth deployments have important attack surfaces when run with AI agents working under delegated authority, and offer design solutions that ensure security guarantees are not lost, while maintaining the benefits of AI through automation.
Serverless Migration Patterns for Compliance-Critical Financial Applications on Microsoft Azure
Migrating compliance-critical financial applications to serverless cloud architectures requires navigating a complex intersection of technical modernization, regulatory obligation, and operational continuity. This paper presents a systematic pattern catalog for migrating on-premises .NET financial applications to Azure serverless infrastructure, covering Azure Function App design, event-driven workflow decomposition, secret management via Azure Key Vault, and compliance-preserving logging and audit trail architectures. Each migration pattern is analyzed against regulatory requirements common in financial services environments including data residency, auditability, and access control mandates. Risk quantification methods are introduced for each migration phase, enabling organizations to bound and communicate migration risk to regulatory stakeholders. Validation is drawn from financial services modernization engagements across multiple enterprise environments.
Intelligent IoT-Enabled Deep Learning System for Advanced Cybersecurity Anomaly Detection
The Internet of Things (IoT) is rapidly increasing attack surface of cyber threats, making the existing security measures insufficient to detect complex and dynamic abnormalities. The article presents a plan for a sophisticated system that uses deep learning (DL) and the internet of things (IoT) to identify cybersecurity irregularities using a CNN-BiLSTM architecture. Convolutional Neural Networks (CNNs) easily extract geographic information, whereas Bidirectional Long Short-Term Memory (BiLSTM) networks detect temporal correlations in network data. After label encoding, normalization, and SMOTE-based data balancing, the proposed model is evaluated on the UNSW-NB15 dataset, thereby increasing its consistency. As shown in experiments, the suggested hybrid model has a higher performance rate as it reaches 99.3% testing accuracy (acc), high precisions (prec), recall (rec), and a low FNR, which is significantly better than the traditional machine learning (ML) and standalone models of DL. The system successfully identifies both known and undiscovered cyber threats, making it ideal for real-time IoT applications. This study promotes the idea of scalable, accurate, and intelligent IDS to safeguard modern IoT networks.
Hybrid Indexing for Operationally-Aware Inventory Costing: Simulation and ERP-Based Validation
Dynamic chain of supply with variable service levels and demand volatility must be supported by a mechanism for inventory costs that is adaptive and able to work with enterprise resource planning (ERP). This paper presents a framework for Adaptive Hybrid Indexing for Operationally-Aware Inventory Costing (AHI-OAIC) that combines operational signals, dynamic SKU tiering, and ERP costing structures to provide a responsive inventory costing without the need to move inventory. The framework encompasses simulation-based scenario modelling, weighted cost-to-serve scoring, buffer-driven volatility control and metadata-based cost group assignments that can be used with Oracle EBS R12.2 workflows. The results from experiments on 2,500 synthetic SKUs from promotional surges, carrier disruptions and operational sensitivity scenarios show enhanced cost visibility and profitability differentiation. Results show 61.6% stability in Tier convergence over 30 days, Oracle EBS convergence improvements of 91-97% in respect to flat baseline costing, and convergence on cost differentiation of 2.35× to 4.18× over flat baseline costing. The framework successfully maintained an even margin lift on all operational profiles while ensuring auditability, GAAP compliant cost rollups and ERP-native governance. Findings demonstrate that adaptive hybrid indexing enables scalable, operationally responsive, and financially transparent inventory costing in volatile supply chain environments.