International Journal of Emerging Research in Engineering and Technology
type: channel
language: en
publication name: International Journal of Emerging Research in Engineering and Technology
items: AI-Driven Billing Solutions in SaaS: Efficiency and Accuracy
items: A Scalable Microservices Architecture for Event-Based Sanctions Screening Systems
items: Streaming Feature Stores and Real-Time ML Inference on Cloud-Native Infrastructure
items: Intelligent ETL Orchestration with Reinforcement Learning and Bayesian Optimization
items: Cloud-Native ETL Automation: Leveraging AI/ML to Build Resilient, Self-Healing Data Pipelines
items: Ethical and Regulatory Challenges in Cybersecurity: A Survey of Data Governance and Policy Mechanisms
items: Accelerating AI at Scale: The Strategic Role of HBM and Advanced Memory Architectures in Next-Generation SoC Design
items: Building Intelligent Enterprise Platforms: Integrating Big Data, AI, and Cloud-Native Architectures
items: Intelligent Software Automation Platforms for Sophisticated Quality Engineering Applications
items: Physics-Guided Machine Learning for Rapid Nonlinear Seismic Safety Assessment of Reinforced-Concrete Frame–Wall Buildings
items: An Explainable Generative AI Framework for Insurance Claims Intelligence and Risk Assessment
items: A Governance Framework for Application Programming Interface Lifecycle Management in Large-Scale Enterprise Digital Transformation
items: Machine Learning Based Clinical Trial Performance Prediction for Medical Industry Applications
items: Enterprise AI Copilot Architecture for CRM Systems: Human-AI Collaboration for Sales Intelligence, Customer Operations, and Decision Support
items: A Hybrid Machine Learning and Control-Theoretic Framework for Stability-Assured Resource Management in Large-Scale Cloud Computing Environments
items: Cloud Data Engineering Strategies for Large-Scale Financial Data Integration and Intelligent Corporate Performance Reporting
items: A Unified Framework for Secure Data Platforms: Combining Data Engineering, AI Analytics, and Intelligent Threat Detection
items: Runtime Integration Failure Modes in Module Federation A Taxonomy and Mitigation Patterns
items: Applying 8D to Customer Complaints: Turning Negative Feedback into Process Improvement
items: A Hybrid MIP-ML Framework for Real-Time 3D Bin Packing in High-Volume E-Commerce Fulfillment
items: SAP S/4HANA Transformation in the Electric Power and Grid Utility Sector: Combination Migration Strategy and Customer-Managed Deployment A Practitioner's Analysis
items: Predictive Failure Intelligence for Enterprise-Scale Data Engineering: A Proactive Framework for Monitoring, Forecasting, and Preventing Data Pipeline Disruptions
items: AI-Assisted Requirements Engineering for Agile Software Development: A Transformer-Based Framework for Requirement Classification, Conflict Detection, and Traceability
items: Agentic AI for Autonomous Software Maintenance: A Retrieval-Augmented Multi-Agent Framework for Bug Localization, Patch Generation, and Regression Validation
items: Generative AI Integration Patterns for Enterprise Workflow Automation: A Practitioner Framework
items: Redux State Management Patterns for Large-Scale React.js Enterprise Applications: An Empirical Analysis
items: OAuth 2.0 Security Patterns for AI-Augmented Microservices: Challenges and Design Principles
items: Serverless Migration Patterns for Compliance-Critical Financial Applications on Microsoft Azure
items: Intelligent IoT-Enabled Deep Learning System for Advanced Cybersecurity Anomaly Detection
items: Hybrid Indexing for Operationally-Aware Inventory Costing: Simulation and ERP-Based Validation

AI-Driven Billing Solutions in SaaS: Efficiency and Accuracy
type: item
description: 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 highligh…
subject: Articles
  topic: section
subject: AI-Driven Billing Solutions
  topic: keywords
subject: Efficiency
  topic: keywords
subject: Accuracy
  topic: keywords
subject: Automation
  topic: keywords
subject: Subscription Models
  topic: keywords
subject: Revenue Leakage
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Traditional Billing
  topic: keywords
creator: R. Vishwa
rights: Copyright (c) 2023
date: 2023-12-30
publication date: 2023-12-30
starting page: 220
ending page: 228
doi: 10.63282/3050-922X.IJERET-V4I4P124

A Scalable Microservices Architecture for Event-Based Sanctions Screening Systems
type: item
description: 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, mai…
subject: Articles
  topic: section
subject: Microservices
  topic: keywords
subject: Event-Driven Architecture
  topic: keywords
subject: Sanctions Screening
  topic: keywords
subject: Financial Compliance
  topic: keywords
subject: Aml
  topic: keywords
subject: Kafka
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Containerization
  topic: keywords
subject: Real-Time Processing
  topic: keywords
creator: Zaheer Syed
creator: Hani Patel
creator: Juwaria Naaz
rights: Copyright (c) 2026
date: 2026-07-08
publication date: 2026-07-08
starting page: 64
ending page: 73
doi: 10.63282/3050-922X.IJERET-V7I3P106

Streaming Feature Stores and Real-Time ML Inference on Cloud-Native Infrastructure
type: item
description: 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.
subject: Articles
  topic: section
subject: Streaming Feature Store
  topic: keywords
subject: Real-Time Machine Learning
  topic: keywords
subject: Online Inference
  topic: keywords
subject: Cloud-Native
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: Kafka
  topic: keywords
subject: Flink
  topic: keywords
subject: Feature Engineering
  topic: keywords
subject: MLOps
  topic: keywords
subject: Low-Latency ML
  topic: keywords
subject: Serverless ML
  topic: keywords
creator: Sivadeep Katangoori
rights: Copyright (c) 2025
date: 2025-03-30
publication date: 2025-03-30
starting page: 158
ending page: 167
doi: 10.63282/3050-922X.IJERET-V6I1P119

Intelligent ETL Orchestration with Reinforcement Learning and Bayesian Optimization
type: item
description: 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…
subject: Articles
  topic: section
subject: ETL
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Bayesian Optimization
  topic: keywords
subject: Orchestration
  topic: keywords
subject: Data Pipelines
  topic: keywords
subject: Automation
  topic: keywords
subject: Hyperparameter Tuning
  topic: keywords
subject: Data Engineering
  topic: keywords
subject: Workflow Optimization
  topic: keywords
subject: Intelligent Scheduling
  topic: keywords
creator: Sivadeep Katangoori
creator: Anudeep Katangoori
rights: Copyright (c) 2023
date: 2023-12-30
publication date: 2023-12-30
starting page: 208
ending page: 219
doi: 10.63282/3050-922X.IJERET-V4I4P123

Cloud-Native ETL Automation: Leveraging AI/ML to Build Resilient, Self-Healing Data Pipelines
type: item
description: 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,…
subject: Articles
  topic: section
subject: Cloud-Native
  topic: keywords
subject: ETL
  topic: keywords
subject: Data Pipelines
  topic: keywords
subject: AI/ML Automation
  topic: keywords
subject: Self-Healing Systems
  topic: keywords
subject: Data Engineering
  topic: keywords
subject: Resiliency
  topic: keywords
subject: Data Quality
  topic: keywords
subject: Fault-Tolerant Pipelines
  topic: keywords
creator: Sivadeep Katangoori
creator: Sandeep Musinipally
rights: Copyright (c) 2021
date: 2021-12-30
publication date: 2021-12-30
starting page: 112
ending page: 121
doi: 10.63282/3050-922X.IJERET-V2I4P112

Ethical and Regulatory Challenges in Cybersecurity: A Survey of Data Governance and Policy Mechanisms
type: item
description: 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.
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Policy Mechanisms
  topic: keywords
subject: Ethical AI
  topic: keywords
subject: Privacy
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
subject: Data Security
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Governance Frameworks
  topic: keywords
creator: Neha Upadhyay
rights: Copyright (c) 2026
date: 2026-07-06
publication date: 2026-07-06
starting page: 56
ending page: 63
doi: 10.63282/3050-922X.IJERET-V7I3P105

Accelerating AI at Scale: The Strategic Role of HBM and Advanced Memory Architectures in Next-Generation SoC Design
type: item
description: 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…
subject: Articles
  topic: section
subject: Artificial Intelligence Accelerators
  topic: keywords
subject: High Bandwidth Memory (HBM)
  topic: keywords
subject: System-On-Chip (Soc)
  topic: keywords
subject: Memory Architecture
  topic: keywords
subject: AI Computing
  topic: keywords
subject: Chiplet Design
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Data-Centric Computing
  topic: keywords
creator: Karthik Allam
rights: Copyright (c) 2023
date: 2023-09-30
publication date: 2023-09-30
starting page: 171
ending page: 181
doi: 10.63282/3050-922X.IJERET-V4I3P118

Building Intelligent Enterprise Platforms: Integrating Big Data, AI, and Cloud-Native Architectures
type: item
description: 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.
subject: Articles
  topic: section
subject: Intelligent Enterprise Platforms
  topic: keywords
subject: Big Data Analytics
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Cloud-Native Architecture
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Microservices
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Data Engineering
  topic: keywords
subject: Enterprise Intelligence
  topic: keywords
creator: Karthik Allam
rights: Copyright (c) 2024
date: 2024-03-30
publication date: 2024-03-30
starting page: 177
ending page: 187
doi: 10.63282/3050-922X.IJERET-V5I1P120

Intelligent Software Automation Platforms for Sophisticated Quality Engineering Applications
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence-Based Software Automation
  topic: keywords
subject: Intelligent Quality Engineering Systems
  topic: keywords
subject: Multi-Agent Architectures
  topic: keywords
subject: Quality Learning Techniques
  topic: keywords
subject: Artificial Intelligence-Enabled Test Optimization
  topic: keywords
creator: Karthik Ramamurthy
rights: Copyright (c) 2025
date: 2025-06-10
publication date: 2025-06-10
starting page: 148
ending page: 157
doi: 10.63282/3050-922X.IJERET-V6I2P118

Physics-Guided Machine Learning for Rapid Nonlinear Seismic Safety Assessment of Reinforced-Concrete Frame–Wall Buildings
type: item
description: 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…
subject: Articles
  topic: section
subject: Reinforced-Concrete Buildings
  topic: keywords
subject: Nonlinear Seismic Analysis
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Rapid Seismic Safety Assessment
  topic: keywords
subject: ETABS
  topic: keywords
subject: Inter-Storey Drift
  topic: keywords
subject: Performance-Based Earthquake Engineering
  topic: keywords
subject: Structural Health Monitoring
  topic: keywords
creator: Abdullah Youssef
rights: Copyright (c) 2026
date: 2026-07-04
publication date: 2026-07-04
starting page: 14
ending page: 20
doi: 10.63282/3050-922X.IJERET-V7I3P104

An Explainable Generative AI Framework for Insurance Claims Intelligence and Risk Assessment
type: item
description: 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.
subject: Articles
  topic: section
subject: Insurance Claims Intelligence
  topic: keywords
subject: Insurance Risk Assessment
  topic: keywords
subject: Explainable Artificial Intelligence (XAI)
  topic: keywords
subject: Generative AI and Fraud Detection
  topic: keywords
creator: Kousik Mallick
rights: Copyright (c) 2026
date: 2026-07-04
publication date: 2026-07-04
starting page: 17
ending page: 25
doi: 10.63282/3050-922X.IJERET-V7I3P103

A Governance Framework for Application Programming Interface Lifecycle Management in Large-Scale Enterprise Digital Transformation
type: item
description: 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, fas…
subject: Articles
  topic: section
subject: API Governance
  topic: keywords
subject: API Lifecycle Management
  topic: keywords
subject: Enterprise Digital Transformation
  topic: keywords
subject: Security Enforcement
  topic: keywords
subject: Versioning and Deprecation Strategy
  topic: keywords
creator: Ganesh Kumar Gangannagari Gangannagari
rights: Copyright (c) 2026
date: 2026-07-02
publication date: 2026-07-02
starting page: 10
ending page: 16
doi: 10.63282/3050-922X.IJERET-V7I3P102

Machine Learning Based Clinical Trial Performance Prediction for Medical Industry Applications
type: item
description: 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.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Clinical Decision Support
  topic: keywords
subject: Risk Stratification
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Electronic Health Records
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Predictive Modeling
  topic: keywords
creator: Nilesh Jain
rights: Copyright (c) 2026
date: 2026-07-02
publication date: 2026-07-02
starting page: 1
ending page: 9
doi: 10.63282/3050-922X.IJERET-V7I3P101

Enterprise AI Copilot Architecture for CRM Systems: Human-AI Collaboration for Sales Intelligence, Customer Operations, and Decision Support
type: item
description: 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 comp…
subject: Articles
  topic: section
subject: Enterprise AI
  topic: keywords
subject: CRM Systems
  topic: keywords
subject: AI Copilot Architecture
  topic: keywords
subject: Human-AI Collaboration
  topic: keywords
subject: Sales Intelligence
  topic: keywords
subject: Customer Operations
  topic: keywords
subject: Decision Support
  topic: keywords
subject: Retrieval-Augmented Generation
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Customer Relationship Management
  topic: keywords
subject: Generative AI Governance
  topic: keywords
creator: Achuta Krishna Kishore Varma Alluri
rights: Copyright (c) 2024
date: 2024-12-30
publication date: 2024-12-30
starting page: 203
ending page: 212
doi: 10.63282/3050-922X.IJERET-V5I4P121

A Hybrid Machine Learning and Control-Theoretic Framework for Stability-Assured Resource Management in Large-Scale Cloud Computing Environments
type: item
description: 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 fo…
subject: Articles
  topic: section
subject: Cloud Computing
  topic: keywords
subject: Autoscaling
  topic: keywords
subject: Resource Management
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Control Theory
  topic: keywords
subject: Stability Assurance
  topic: keywords
subject: Model Predictive Control
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: Workload Forecasting
  topic: keywords
subject: Service-Level Objectives
  topic: keywords
creator: Nilesh Mutyam
rights: Copyright (c) 2023
date: 2023-12-30
publication date: 2023-12-30
starting page: 198
ending page: 207
doi: 10.63282/3050-922X.IJERET-V4I4P122

Cloud Data Engineering Strategies for Large-Scale Financial Data Integration and Intelligent Corporate Performance Reporting
type: item
description: 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…
subject: Articles
  topic: section
subject: Cloud Data Engineering
  topic: keywords
subject: Financial Data Integration
  topic: keywords
subject: Corporate Performance Reporting
  topic: keywords
subject: Data Lakehouse
  topic: keywords
subject: Data Warehouse
  topic: keywords
subject: Etl/Elt
  topic: keywords
subject: Real-Time Analytics
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Financial Analytics
  topic: keywords
subject: Cloud Computing
  topic: keywords
creator: Ramakrishna Taluri
rights: Copyright (c) 2022
date: 2022-12-30
publication date: 2022-12-30
starting page: 176
ending page: 188
doi: 10.63282/3050-922X.IJERET-V3I4P119

A Unified Framework for Secure Data Platforms: Combining Data Engineering, AI Analytics, and Intelligent Threat Detection
type: item
description: 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-bas…
subject: Articles
  topic: section
subject: Data Engineering
  topic: keywords
subject: Artificial Intelligence Analytics
  topic: keywords
subject: Intelligent Threat Detection
  topic: keywords
subject: Secure Data Platforms
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Enterprise Data Ecosystems
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Security Intelligence
  topic: keywords
creator: P. Bastin Thiyagaraj
rights: Copyright (c) 2026
date: 2026-06-03
publication date: 2026-06-03
starting page: 292
ending page: 298
doi: 10.63282/3050-922X.IJERET-V7I2P135

Runtime Integration Failure Modes in Module Federation A Taxonomy and Mitigation Patterns
type: item
description: 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.
subject: Articles
  topic: section
subject: Module Federation
  topic: keywords
subject: Webpack 5
  topic: keywords
subject: Micro-Frontend
  topic: keywords
subject: Runtime Integration Failure
  topic: keywords
subject: Shared Scope
  topic: keywords
subject: Version Conflict
  topic: keywords
subject: Singleton Enforcement
  topic: keywords
subject: Async Bootstrap
  topic: keywords
subject: Federated Module Lifecycle
  topic: keywords
subject: Javascript Dependency Isolation
  topic: keywords
subject: Frontend Reliability
  topic: keywords
subject: Distributed Frontend Systems
  topic: keywords
creator: Parth Patel
rights: Copyright (c) 2024
date: 2024-06-30
publication date: 2024-06-30
starting page: 211
ending page: 218
doi: 10.63282/3050-922X.IJERET-V5I2P121

Applying 8D to Customer Complaints: Turning Negative Feedback into Process Improvement
type: item
description: 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.
subject: Articles
  topic: section
subject: 8d Methodology
  topic: keywords
subject: Customer Complaints
  topic: keywords
subject: Root Cause Analysis
  topic: keywords
subject: Continuous Improvement
  topic: keywords
subject: Automotive Spice
  topic: keywords
subject: Quality Management
  topic: keywords
creator: Krishna Chaitanya Mamidala
rights: Copyright (c) 2023
date: 2023-06-30
publication date: 2023-06-30
starting page: 181
ending page: 185
doi: 10.63282/3050-922X.IJERET-V4I2P119

A Hybrid MIP-ML Framework for Real-Time 3D Bin Packing in High-Volume E-Commerce Fulfillment
type: item
description: 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 in…
subject: Articles
  topic: section
subject: 3D Bin Packing
  topic: keywords
subject: Mixed Integer Programming
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Combinatorial Optimization
  topic: keywords
subject: E-Commerce Fulfillment
  topic: keywords
subject: NP-Hard Problems
  topic: keywords
subject: Operations Research
  topic: keywords
subject: Supply Chain Optimization
  topic: keywords
subject: Real-Time Decision Systems
  topic: keywords
subject: Packaging Optimization
  topic: keywords
creator: Venkatesh Manohar
rights: Copyright (c) 2023
date: 2023-06-30
publication date: 2023-06-30
starting page: 170
ending page: 180
doi: 10.63282/3050-922X.IJERET-V4I2P118

SAP S/4HANA Transformation in the Electric Power and Grid Utility Sector: Combination Migration Strategy and Customer-Managed Deployment A Practitioner's Analysis
type: item
description: 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.
subject: Articles
  topic: section
subject: SAP S/4HANA
  topic: keywords
subject: ERP Migration
  topic: keywords
subject: Electric Utility
  topic: keywords
subject: Greenfield
  topic: keywords
subject: Brownfield
  topic: keywords
subject: Combination Migration
  topic: keywords
subject: IS-U
  topic: keywords
subject: SAP Utilities
  topic: keywords
subject: Enterprise Asset Management
  topic: keywords
subject: Plant Maintenance
  topic: keywords
subject: NERC-CIP
  topic: keywords
subject: Grid Modernization
  topic: keywords
subject: Work Order Management
  topic: keywords
subject: Clean Core
  topic: keywords
subject: Total Cost of Ownership
  topic: keywords
subject: Customer-Managed Deployment
  topic: keywords
creator: Gururaj Veershetty
rights: Copyright (c) 2023
date: 2023-03-30
publication date: 2023-03-30
starting page: 218
ending page: 227
doi: 10.63282/3050-922X.IJERET-V4I1P123

Predictive Failure Intelligence for Enterprise-Scale Data Engineering: A Proactive Framework for Monitoring, Forecasting, and Preventing Data Pipeline Disruptions
type: item
description: 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, p…
subject: Articles
  topic: section
subject: Predictive Failure Intelligence
  topic: keywords
subject: Data Engineering
  topic: keywords
subject: Data Pipeline Reliability
  topic: keywords
subject: Data Observability
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: AIops
  topic: keywords
subject: Data Quality
  topic: keywords
subject: Mlops
  topic: keywords
subject: SRE
  topic: keywords
subject: Proactive Monitoring
  topic: keywords
subject: Enterprise Architecture
  topic: keywords
creator: Vineeth Kumar Reddy Mittamidi
rights: Copyright (c) 2023
date: 2023-12-03
publication date: 2023-12-03
starting page: 188
ending page: 197
doi: 10.63282/3050-922X.IJERET-V4I4P121

AI-Assisted Requirements Engineering for Agile Software Development: A Transformer-Based Framework for Requirement Classification, Conflict Detection, and Traceability
type: item
description: 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 adopt…
subject: Articles
  topic: section
subject: Requirements Engineering
  topic: keywords
subject: Agile Software Development
  topic: keywords
subject: Transformers
  topic: keywords
subject: BERT
  topic: keywords
subject: Conflict Detection
  topic: keywords
subject: Requirement Classification
  topic: keywords
subject: Traceability
  topic: keywords
subject: Software Governance
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Natural Language Processing
  topic: keywords
creator: S. Bhaskaran
creator: GR. Brindha
creator: A Emily Jenifer
creator: R. Kalai Vazhi
rights: Copyright (c) 2024
date: 2024-12-30
publication date: 2024-12-30
starting page: 192
ending page: 202
doi: 10.63282/3050-922X.IJERET-V5I4P120

Agentic AI for Autonomous Software Maintenance: A Retrieval-Augmented Multi-Agent Framework for Bug Localization, Patch Generation, and Regression Validation
type: item
description: 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, regres…
subject: Articles
  topic: section
subject: Agentic AI
  topic: keywords
subject: Autonomous Software Maintenance
  topic: keywords
subject: Retrieval-Augmented Generation
  topic: keywords
subject: Bug Localization
  topic: keywords
subject: Automated Program Repair
  topic: keywords
subject: Regression Validation
  topic: keywords
subject: Multi-Agent Systems
  topic: keywords
subject: CI/CD Governance
  topic: keywords
creator: Swathi .J .N
creator: S. L. Aarthy
creator: S. Ananda Kumar
creator: Debi Prasanna Acharjya
rights: Copyright (c) 2026
date: 2026-06-01
publication date: 2026-06-01
starting page: 282
ending page: 291
doi: 10.63282/3050-922X.IJERET-V7I2P134

Generative AI Integration Patterns for Enterprise Workflow Automation: A Practitioner Framework
type: item
description: 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.…
subject: Articles
  topic: section
subject: Generative AI
  topic: keywords
subject: Workflow Automation
  topic: keywords
subject: Enterprise Integration
  topic: keywords
subject: AI Patterns
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Business Process Automation
  topic: keywords
subject: Human-in-the-Loop
  topic: keywords
subject: AI Architecture
  topic: keywords
subject: Intelligent Systems
  topic: keywords
subject: Digital Transformation
  topic: keywords
creator: Gnana Nishitha Chowdary Aluri
creator: Venkatesh Manohar
rights: Copyright (c) 2025
date: 2025-09-25
publication date: 2025-09-25
starting page: 165
ending page: 172
doi: 10.63282/3050-922X.IJERET-V6I3P121

Redux State Management Patterns for Large-Scale React.js Enterprise Applications: An Empirical Analysis
type: item
description: 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 a…
subject: Articles
  topic: section
subject: Redux
  topic: keywords
subject: React.js
  topic: keywords
subject: State Management
  topic: keywords
subject: Enterprise Frontend
  topic: keywords
subject: Performance Optimization
  topic: keywords
subject: JavaScript
  topic: keywords
subject: Single Page Applications
  topic: keywords
subject: Memoization
  topic: keywords
subject: Frontend Architecture
  topic: keywords
subject: Software Maintainability
  topic: keywords
creator: Gnana Nishitha Chowdary Aluri
creator: Hari Krishna Mupparapu
rights: Copyright (c) 2024
date: 2024-06-30
publication date: 2024-06-30
starting page: 201
ending page: 210
doi: 10.63282/3050-922X.IJERET-V5I2P120

OAuth 2.0 Security Patterns for AI-Augmented Microservices: Challenges and Design Principles
type: item
description: 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.
subject: Articles
  topic: section
subject: OAuth 2.0
  topic: keywords
subject: AI Agents
  topic: keywords
subject: Microservices Security
  topic: keywords
subject: Zero Trust Architecture
  topic: keywords
subject: Token Management
  topic: keywords
subject: Enterprise Security
  topic: keywords
subject: Agentic Systems
  topic: keywords
subject: Service Mesh
  topic: keywords
subject: Identity Management
  topic: keywords
subject: Authorization Patterns
  topic: keywords
creator: Gnana Nishitha Chowdary Aluri
rights: Copyright (c) 2022
date: 2022-06-30
publication date: 2022-06-30
starting page: 221
ending page: 230
doi: 10.63282/3050-922X.IJERET-V3I2P122

Serverless Migration Patterns for Compliance-Critical Financial Applications on Microsoft Azure
type: item
description: 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.
subject: Articles
  topic: section
subject: Serverless Architecture
  topic: keywords
subject: Azure Functions
  topic: keywords
subject: Cloud Migration
  topic: keywords
subject: Compliance Systems
  topic: keywords
subject: Financial Applications
  topic: keywords
subject: .NET Core
  topic: keywords
subject: Azure Key Vault
  topic: keywords
subject: Audit Trails
  topic: keywords
subject: Risk Quantification
  topic: keywords
subject: Application Modernization
  topic: keywords
creator: Hari Krishna Mupparapu
creator: Chandra K Movva
rights: Copyright (c) 2020
date: 2020-06-30
publication date: 2020-06-30
starting page: 85
ending page: 96
doi: 10.63282/3050-922X.IJERET-V1I2P111

Intelligent IoT-Enabled Deep Learning System for Advanced Cybersecurity Anomaly Detection
type: item
description: 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.
subject: Articles
  topic: section
subject: IoT Security
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: CNN
  topic: keywords
subject: BiLSTM
  topic: keywords
subject: Intrusion Detection System
  topic: keywords
subject: UNSW-NB15
  topic: keywords
subject: SMOTE
  topic: keywords
subject: Network Security
  topic: keywords
creator: Vikas Kumar Pandey
rights: Copyright (c) 2026
date: 2026-05-29
publication date: 2026-05-29
starting page: 273
ending page: 281
doi: 10.63282/3050-922X.IJERET-V7I2P133

Hybrid Indexing for Operationally-Aware Inventory Costing: Simulation and ERP-Based Validation
type: item
description: 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.
subject: Articles
  topic: section
subject: Adaptive Inventory Costing
  topic: keywords
subject: Hybrid Indexing
  topic: keywords
subject: ERP Integration
  topic: keywords
subject: Operational Analytics
  topic: keywords
subject: Cost Tiering
  topic: keywords
creator: Naga Venkata Sriram
rights: Copyright (c) 2026
date: 2026-05-27
publication date: 2026-05-27
starting page: 262
ending page: 272
doi: 10.63282/3050-922X.IJERET-V7I2P132