International Journal of Research Publications in Engineering, Technology and Management (IJRPETM)
type: channel
language: en
publication name: International Journal of Research Publications in Engineering, Technology and Management (IJRPETM)
items: Autonomous Social Welfare Systems Using Agentic AI for Intelligent Case Management and Resource Allocation
items: Adaptive Risk Based Access Control in Cloud Native Banking Platforms: A Zero Trust Framework for Dynamic Identity Enforcement
items: Cognitive Integration Architectures: Unifying AI, Event Streaming, and API Management for Real-Time Enterprise Systems
items: Generative AI for Smart Agricultural Equipment Automation and Pharmaceutical Crop Yield Prediction
items: Synergy - Integrated Collaborative Code Workspace using Smart AI
items: CyberGuard - An AI-Enabled System for Cyberbullying Detection and Real-Time Cybercrime Alerts
items: Wastewater-Based Circular Economy: Water Reuse in Agriculture and Industry — A Comprehensive Review
items: An IoT-Based Smart Helmet for Real Time Rider Safety Monitoring and Emergency Response System
items: From Reactive to Proactive: Engineering AI-First Reliability for SAP Mission-Critical Workloads
items: Low-Code Mobile Application Builder with AI-Assisted Features using Flutter & Firebase
items: AI Driven Secure Cloud Ecosystems for Federated Intelligence Cyber Resilience and Enterprise Data Governance
items: Lineage, Traceability, and Reproducibility as Reliability Requirements in Enterprise AI Systems
items: Federated Learning Architecture for Privacy Preserving Healthcare Data Governance and Scalable Cloud Native Intelligent Systems
items: Building Intelligent Systems from Data Silos Enabling Trustworthy AI and Cloud Ecosystems for Modern Healthcare
items: A Decentralized Security Model for Preventing Data Breaches in Distributed Environments
items: A Scalable Microservice Framework for Multi-Modal Logistics Route Optimization
items: Smart Artificial Intelligence Framework for Cloud Centric Systems and Continuous Threat Intelligence Evolution
items: AI-Enabled Financial Data Governance Using a Secure Cloud-Based Framework for Enterprise Compliance and Automation
items: How Servicenow Impacted Accelerating Clinical Trials
items: BUSINESS VALUE OF SERVICENOW FOR HEALTH CARE AND EDUCATION SERVICES
items: Servicenow Create Enterprise Workflows for Various Digitalize Business Processes
items: PyHadoopLake: A Python-Native Framework for Building Scalable Lakehouse Architectures on Hadoop
items: Designing Self Adaptive Cloud and AI Platforms for High Performance Secure and Intelligent Systems
items: BUILDING SECURE MULTI-CLOUD INFRASTRUCTURE FOR MISSION-CRITICAL ENTERPRISE WORKLOADS
items: AI-ENABLED DIGITAL IDENTITY VERIFICATION FRAMEWORK FOR GOVERNMENT SELF-SERVICE PLATFORMS USING SECURE API AND CLOUD INTEGRATION
items: Governance Led Security Architecture in Large Scale Enterprise Systems
items: Integrating Machine Learning with Business Rule Management Systems for Adaptive Enterprise.
items: A Multi-Layer AI-Driven Decision Intelligence Framework for Enterprise and Healthcare System
items: Building Foundational Data Integrity in Enterprise Retail Systems: A Structured Approach to Early-Stage Data Governance
items: ENSURING DATA INTEGRITY AND OPERATIONAL CONTINUITY IN MISSION-CRITICAL ENTERPRISE PLATFORM MIGRATIONS: AN ARCHITECTURAL FRAMEWORK

Autonomous Social Welfare Systems Using Agentic AI for Intelligent Case Management and Resource Allocation
type: item
description: Social welfare programs play a crucial role in supporting vulnerable populations by providing financial assistance, healthcare access, housing support, and other essential services. However, traditional welfare management systems often face challenges such as manual case processing, fragmented data sources, delayed decision-making, and inefficient resource allocation. These limitations can lead to delays in service delivery, increased administrative burden, and reduced effectiveness of welfare programs. Recent advancements in artificial intelligence, particularly Agentic AI, offer new opportunities to modernize social welfare systems. Agentic AI refers to autonomous AI systems capable of reasoning, decision-making, and executing tasks with minimal human intervention. By integrating intelligent agents into social welfare platforms, governments and organizations can automate case management processes, analyze large-scale socio-economic data, and dynamically allocate resources to individuals and communities based on real-time needs. This article explores the architecture and operational framework of autonomous social welfare systems powered by Agentic AI. The proposed approach combines multi-agent systems, machine learning models, and cloud-based data infrastructure to enable intelligent case assessment, eligibility verification, predictive risk analysis, and automated service recommendations. The framework also emphasizes transparency, accountability, and ethical considerations to ensure fairness and privacy protection in automated decision-making. Furthermore, the study discusses potential system components such as data ingestion pipelines, AI-driven decision engines, citizen interaction interfaces, and policy compliance modules. Conceptual diagrams and tables illustrate how agent-based workflows can improve efficiency, reduce operational costs, and enhance service delivery across large-scale public welfare programs. The findings suggest that integrating Agentic AI i…
subject: Articles
  topic: section
subject: Agentic Artificial Intelligence
  topic: keywords
subject: Autonomous Welfare Systems
  topic: keywords
subject: Intelligent Case Management
  topic: keywords
subject: Social Welfare Automation
  topic: keywords
subject: Multi-Agent Systems
  topic: keywords
subject: Resource Allocation Optimization
  topic: keywords
subject: AI-driven Public Services
  topic: keywords
subject: Predictive Social Analytics
  topic: keywords
subject: Digital Government Platforms
  topic: keywords
subject: Ethical AI Governance
  topic: keywords
creator: Ganesh Adepu
rights: Copyright (c) 2026
date: 2026-03-30
publication date: 2026-03-30
starting page: 651
ending page: 654
doi: 10.15662/r8jb9752

Adaptive Risk Based Access Control in Cloud Native Banking Platforms: A Zero Trust Framework for Dynamic Identity Enforcement
type: item
description: Conventional access control systems used in the banking sector are based on unchanging roles and cannot work in the dynamic cloud settings. An Adaptive Risk Based Access Control framework of cloud-native banking platforms based on Zero Trust principles is suggested. The system considers real-time identity signals, like device, behavior, network, and transaction risk to decide on continuous access. Findings indicate that the performance of the system is significantly better than that of RBAC as there is a drop-in false acceptance rate (8.6% to 2.1%), over-privileged access (18.4% to 5.7%) and an increase in detection rate (61.3% to 91.8%). The completion of audits increased to 96.7% and there were enhanced adherence and protection in financial systems
subject: Articles
  topic: section
subject: Adaptive Risk-Based Access Control
  topic: keywords
subject: Cloud-Native Banking Security
  topic: keywords
subject: Zero Trust Architecture
  topic: keywords
subject: Identity and Access Management (IAM)
  topic: keywords
creator: Raja Mohan Dhanushkodi
rights: Copyright (c) 2024
date: 2024-10-10
publication date: 2024-10-10
starting page: 11289
ending page: 11296
doi: 10.15662/IJRPETM.2024.0705016

Cognitive Integration Architectures: Unifying AI, Event Streaming, and API Management for Real-Time Enterprise Systems
type: item
description: Modern enterprise ecosystems are undergoing a fundamental transformation driven by the convergence of Artificial Intelligence (AI), event-driven architectures, and advanced API management platforms. As organizations increasingly operate in distributed, hybrid, and multi-cloud environments, traditional integration approaches—based on batch processing, static workflows, and tightly coupled middleware—are becoming insufficient to meet the demands of real-time responsiveness, scalability, and intelligence-driven decision-making This article proposes the concept of Cognitive Integration Architectures (CIA), a unified architectural paradigm that integrates AI capabilities, event streaming infrastructures, and API management layers into a cohesive, adaptive, and self-optimizing enterprise integration fabric. The cognitive layer introduces intelligence into integration workflows by enabling real-time event interpretation, predictive routing, anomaly detection, and autonomous decision orchestration. Event streaming platforms serve as the backbone for high-throughput, low-latency data movement, enabling continuous data flow across microservices, applications, and analytics systems. API management systems provide governance, security enforcement, traffic control, and lifecycle management for enterprise services exposed across internal and external ecosystems The proposed architecture shifts enterprise integration from a deterministic and rule-based model to a context-aware, AI-augmented, and event-native paradigm, capable of dynamically adapting to workload variations, system failures, and evolving business requirements. The article further examines design principles, architectural patterns, and real-world implementation strategies for deploying cognitive integration systems in cloud-native and hybrid enterprise environments Key benefits of this approach include improved operational intelligence, reduced latency in decision pipelines, enhanced scalability, proactive fault dete…
subject: Articles
  topic: section
subject: Cognitive Integration Architecture
  topic: keywords
subject: AI-Augmented Integration
  topic: keywords
subject: Event Streaming
  topic: keywords
subject: Real-Time Enterprise Systems
  topic: keywords
subject: API Management
  topic: keywords
subject: Event-Driven Architecture
  topic: keywords
subject: Microservices Integration
  topic: keywords
subject: Intelligent Middleware
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: Cloud-Native Architecture
  topic: keywords
subject: Apache Kafka
  topic: keywords
subject: Data Orchestration
  topic: keywords
subject: Adaptive Systems
  topic: keywords
subject: Enterprise AI
  topic: keywords
creator: Hariprakash Pasumarthi
rights: Copyright (c) 2026
date: 2026-05-10
publication date: 2026-05-10
starting page: 1059
ending page: 1063
doi: 10.15662/388s5433

Generative AI for Smart Agricultural Equipment Automation and Pharmaceutical Crop Yield Prediction
type: item
description: Generative AI shows promise in automating intelligent field equipment, enhancing the efficiency of physical work in agriculture. Current research in agricultural machinery automation, encompassing both field equipment and robotic systems, has witnessed groundbreaking advancements aimed at addressing the increasing labor shortage in agriculture. Additionally, autonomous vehicle research has achieved considerable deployment within government and industrial robotics programs. Farm machinery automation primarily employs symbiotic, classical, task-oriented AI techniques, while robotic harvesting and retrieval remains application-specific. These systems rely heavily on robust and specialized sensors to support perception, state estimation, mapping, planning, and control for real-time operation in unstructured outdoor environments. The second area influenced by GAI is pharmaceutical crop yield prediction. Yield forecasting systems tailored to the pharmaceutical supply chain for cannabis and hemp production have recently emerged. These systems leverage novel generative AI techniques for modelling crop growth and inferring phenotypic characteristics through data fusion involving multispectral and hyperspectral imaging, alongside 2D, 3D, and 4D imaging. A scalable system architecture for GAI implementation in agricultural applications has been presented, addressing aspects related to data pipelines, associated sensor infrastructure, and an edge device network for reducing latency, bandwidth requirements, and privacy concerns.
subject: Articles
  topic: section
subject: Generative Artificial Intelligence
  topic: keywords
subject: Smart Agricultural Automation
  topic: keywords
subject: Precision Agriculture
  topic: keywords
subject: Pharmaceutical Crop Yield Prediction
  topic: keywords
subject: Machine Learning in Farming
  topic: keywords
subject: Autonomous Agricultural Equipment
  topic: keywords
subject: AI-Based Crop MonitoringPredictive Analytics in Agriculture
  topic: keywords
subject: Intelligent Farming Systems
  topic: keywords
subject: Sustainable Agricultural Technologies
  topic: keywords
creator: Sathya Kannan
rights: Copyright (c) 2026
date: 2026-05-08
publication date: 2026-05-08
starting page: 1068
ending page: 1074
doi: 10.15662/IJRPETM.2026.0903007

Synergy - Integrated Collaborative Code Workspace using Smart AI
type: item
description: Synergy is an AI-powered collaborative coding platform designe 23d to enhance modern software development and learning through an integrated and intelligent environment. Traditional coding tools often lack real-time collaboration, seamless execution, and built-in intelligent assistance, which can hinder productivity and increase development complexity. Synergy addresses these challenges by providing a unified web-based platform that combines live collaboration, automated execution, AI-driven support, and simplified deployment. The platform enables real-time multi-user code editing, allowing multiple developers to work simultaneously on the same project with minimal latency. All changes are instantly synchronized across users, promoting effective teamwork, peer learning, and collaborative problem-solving. An integrated AI assistant enhances the development experience by offering contextual code suggestions, automated debugging, and clear explanations of programming concepts. The AI component leverages intelligent analysis to understand user intent, detect potential errors, and recommend optimizations, thereby reducing development time and improving code quality. This intelligent support helps users identify errors, optimize code, and better understand complex logic, making both development and learning more efficient. The platform is designed with scalability and security in mind, ensuring that multiple users can collaborate without performance degradation while maintaining data integrity and isolation can collaborate without performance degradation while maintaining data integrity and isolation. Furthermore, the platform includes one-click deployment capabilities, streamlining the process of moving applications from development to production. By integrating deployment pipelines within the same environment, Synergy reduces the dependency on external tools and simplifies the overall software development lifecycle. The system also supports project management features s…
subject: Articles
  topic: section
subject: Real Time Collaboration
  topic: keywords
subject: AI Powered Coding
  topic: keywords
subject: Live Pair Programming
  topic: keywords
subject: Secure Code Execution
  topic: keywords
subject: One Click Deployment
  topic: keywords
subject: Web Based Development Platform
  topic: keywords
creator: Dr V Seedha Devi
creator: Sadhana S, Shalini M, Samitha G
rights: Copyright (c) 2026
date: 2026-05-09
publication date: 2026-05-09
starting page: 1059
ending page: 1067
doi: 10.15662/IJRPETM.2026.0903006

CyberGuard - An AI-Enabled System for Cyberbullying Detection and Real-Time Cybercrime Alerts
type: item
description: Artificial Intelligence (AI) plays a significant role in analyzing large volumes of unstructured data in cybersecurity applications. This paper presents an AI-driven system for detecting cyberbullying and generating automated cybercrime alerts in real time. The proposed system integrates Machine Learning (ML) and Natural Language Processing (NLP) techniques to classify harmful textual content effectively.Text data is preprocessed using tokenization, normalization, stop-word removal, and transformed into numerical features using TF-IDF. Supervised learning algorithms such as Support Vector Machine (SVM) are employed for accurate text classification. The system also supports multilingual input, enabling detection across diverse user interactions.A browser extension is used to capture user-generated content, which is then analyzed by the detection module. Upon identifying abusive or harmful language, the system triggers alerts to notify users or concerned authorities. Additionally, the system leverages modern language models to enhance contextual understanding and improve detection accuracy.This solution aims to provide a scalable and efficient approach to monitor online interactions and mitigate cyberbullying, contributing to safer digital environments.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Cyberbullying Detection
  topic: keywords
subject: Text Classification
  topic: keywords
subject: Multilingual NLP
  topic: keywords
subject: Browser Extension
  topic: keywords
subject: TF-ID
  topic: keywords
subject: Online Harassment Monitoring
  topic: keywords
subject: Chrome Extension
  topic: keywords
creator: Dr. V. Seedha Devi
creator: S. Reshma, P. Suguna
rights: Copyright (c) 2026
date: 2026-05-10
publication date: 2026-05-10
starting page: 1047
ending page: 1058
doi: 10.15662/IJRPETM.2026.0903005

Wastewater-Based Circular Economy: Water Reuse in Agriculture and Industry — A Comprehensive Review
type: item
description: Escalating global freshwater demand driven by climate change, population growth, and industrialization necessitates transformative water management strategies. The shift from linear, end-of-pipe wastewater treatment to circular economy (CE) approaches offers significant opportunities for water security, environmental protection, and economic development. This review synthesizes two decades of research on wastewater-based circular economy (WBCE) systems, focusing on water reuse feasibility, nutrient recovery, and resource recovery potential across agricultural and industrial sectors. Globally, wastewater generation reaches 359.4 billion m³ annually, yet only 63% is collected and 52% is treated before discharge (Jones et al., 2021). This wastewater contains substantial recoverable nutrients—16.6 Tg nitrogen, 3.0 Tg phosphorus, and 6.3 Tg potassium annually—which could offset approximately 13–14% of global agricultural fertilizer demand (Qadir et al., 2020). Advanced treatment technologies such as membrane bioreactors (MBRs) and advanced oxidation processes (AOPs) achieve up to 95% BOD removal, 88% nitrogen removal, and 95% phosphorus removal, significantly outperforming conventional systems (Islam, 2025). Economic assessments indicate that integrated circular systems combining nutrient recovery, energy generation, and water reuse can achieve lower life-cycle costs than conventional treatment over 20 years, despite higher upfront capital investment (Barua, 2025). However, large-scale implementation remains constrained by regulatory fragmentation, financial limitations, technological gaps, and social acceptance barriers. Overall, this review identifies key technological innovations, policy frameworks, and institutional reforms required to scale WBCE systems. Integration of advanced treatment technologies with nature-based solutions and resource recovery strategies enables simultaneous progress toward water security, circular economy transition, climate resilience, and b…
subject: Articles
  topic: section
subject: wastewater reuse
  topic: keywords
subject: circular economy
  topic: keywords
subject: nutrient recovery
  topic: keywords
subject: membrane bioreactors
  topic: keywords
subject: advanced oxidation processes
  topic: keywords
subject: agriculture
  topic: keywords
subject: industrial water reuse
  topic: keywords
subject: struvite crystallization
  topic: keywords
subject: resource recovery
  topic: keywords
subject: sustainable water management
  topic: keywords
creator: Santunu Barua
rights: Copyright (c) 2026
date: 2026-05-07
publication date: 2026-05-07
starting page: 1031
ending page: 1046
doi: 10.15662/IJRPETM.2026.0903004

An IoT-Based Smart Helmet for Real Time Rider Safety Monitoring and Emergency Response System
type: item
description: Two-wheeler accidents contribute significantly to road fatalities due to delayed emergency response and lack of real-time safety monitoring. This paper presents Techmet, an IoT-based smart helmet system designed to enhance rider safety through automated detection and alert mechanisms. The system is divided into four modules: sensing, processing and decision, communication, and control and alert. Sensors such as accelerometer, gyroscope, alcohol sensor, and IR sensor collect real-time data. The processing module analyzes this data to detect accidents, alcohol consumption, and helmet non-compliance. The communication module enables GPS-based location tracking and data transmission, while the control module triggers emergency alerts and manages vehicle ignition. A mobile application provides real-time monitoring and notifications. The system demonstrates improved response time, reliable performance, and a scalable approach to reducing accident-related fatalities.
subject: Articles
  topic: section
subject: IoT
  topic: keywords
subject: Smart Helmet
  topic: keywords
subject: Rider Safety
  topic: keywords
subject: Accident Detection
  topic: keywords
subject: Emergency Alert System
  topic: keywords
subject: GPS Tracking
  topic: keywords
subject: Sensor Integration
  topic: keywords
subject: Real-Time Monitoring
  topic: keywords
subject: Techmet
  topic: keywords
creator: Alangaram S
creator: Yuvaraj G, Srivatsan M.J, Sathish R
rights: Copyright (c) 2026
date: 2026-05-09
publication date: 2026-05-09
starting page: 1021
ending page: 1030
doi: 10.15662/IJRPETM.2026.0903003

From Reactive to Proactive: Engineering AI-First Reliability for SAP Mission-Critical Workloads
type: item
description: Within the last twenty years, SAP systems have evolved from mere transactional support tools into comprehensive enterprise resource platforms supporting millions of simultaneous operations per day. Yet, the enterprise software ecosystem continues to treat reliability as an operational afterthought, relying on fundamentally reactive postures even as monolithic architectures have migrated to highly distributed, mission-critical cloud topologies. Contemporary literature champions Artificial Intelligence for IT Operations (AIOps) and Site Reliability Engineering (SRE) as isolated panaceas, engineering predictive models that identify infrastructural decay but deliberately decouple these insights from the execution authority required to halt it. One must ask: does providing human operators with a more sophisticated alerting mechanism actually prevent catastrophic system failures? It fundamentally does not. To bridge the operational chasm between predicting a failure and surviving it, this research proposes a closed-loop, machine-centric continuum that synthesizes predictive analytics specifically addressing dataset imbalance in rare IT incidents with deterministic, automated remediation pipelines. Rigorous empirical stress-testing via intentional chaos engineering within a simulated SAP environment reveals that dynamically tethering the automated intervention threshold to a depleting SRE error budget rapidly neutralizes cascading anomalies. This significantly compresses recovery times while adhering to strict enterprise compliance constraints. Ultimately, by explicitly engineering the human bottleneck out of the immediate critical path, this foundational framework shifts the objective of systems engineering from reactive firefighting to absolute prevention proving that continuous availability is only achievable when intelligent, agentic orchestration is embedded directly into the infrastructure layer.
subject: Articles
  topic: section
subject: SAP Mission-Critical Workloads
  topic: keywords
subject: AI-First Engineering
  topic: keywords
subject: Site Reliability Engineering (SRE)
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
subject: Automated Remediation
  topic: keywords
subject: AIOps (AI for IT Operations)
  topic: keywords
subject: Zero Downtime Architecture
  topic: keywords
subject: Agentic Orchestration
  topic: keywords
creator: Anuradha Karnam
rights: Copyright (c) 2026
date: 2026-05-06
publication date: 2026-05-06
starting page: 1011
ending page: 1020
doi: 10.15662/IJRPETM.2026.0903002

Low-Code Mobile Application Builder with AI-Assisted Features using Flutter & Firebase
type: item
description: The increasing demand for rapid application development has highlighted the limitations of traditional coding-intensive approaches, particularly in terms of development time, complexity, and accessibility. This project presents the design and implementation of a Low-Code Application Builder, a platform developed to streamline and simplify the process of creating fully functional applications with minimal manual coding. The system provides an intuitive visual interface that enables users to design applications using drag-and-drop components, predefined templates, and configurable elements. It supports key functionalities such as dynamic user interface generation, real-time preview, backend integration, and database connectivity within a unified development environment. This approach allows both technical and non-technical users to efficiently build, modify, and deploy applications.. By incorporating reusable components and automated workflows, the system significantly reduces development effort while maintaining consistency and reliability. In conclusion, the proposed Low-Code Application Builder enhances development efficiency, minimizes technical barriers, and accelerates the application lifecycle, making it a practical solution for modern software development requirements
subject: Articles
  topic: section
subject: Low-Code Development
  topic: keywords
subject: Template-Based Design
  topic: keywords
subject: Backend Customization
  topic: keywords
subject: Database Management
  topic: keywords
subject: Platform
  topic: keywords
subject: Apk
  topic: keywords
subject: Dart
  topic: keywords
subject: Depolyment
  topic: keywords
subject: App Builder
  topic: keywords
subject: Firebase
  topic: keywords
creator: Dr.V.Seedha Devi
creator: Harshini R, Dhana Lakshmi E, Gayathri N, Nithesha P
rights: Copyright (c) 2026
date: 2026-05-06
publication date: 2026-05-06
starting page: 1001
ending page: 1010
doi: 10.15662/IJRPETM.2026.0903001

AI Driven Secure Cloud Ecosystems for Federated Intelligence Cyber Resilience and Enterprise Data Governance
type: item
description: AI-driven secure cloud ecosystems are emerging as a foundational paradigm for modern digital enterprises that rely on distributed computing, federated intelligence, and large-scale data governance. As organizations increasingly adopt multi-cloud and hybrid-cloud infrastructures, ensuring cyber resilience and maintaining robust enterprise data governance have become critical challenges. This paper explores an integrated framework where artificial intelligence (AI) is leveraged to enhance security automation, predictive threat intelligence, and adaptive policy enforcement across federated cloud environments. The study focuses on the convergence of federated learning, zero-trust security models, and intelligent orchestration systems to create resilient cloud ecosystems capable of self-adaptation in the presence of evolving cyber threats. It further examines how AI-enabled analytics can improve data governance by ensuring compliance, data lineage tracking, access control optimization, and privacy-preserving computation. Cyber resilience is addressed through continuous monitoring, anomaly detection, and autonomous response mechanisms that minimize downtime and data exposure risks. The research also highlights governance challenges related to cross-border data flows, regulatory compliance, and distributed identity management. By integrating AI with secure cloud architectures, federated intelligence systems can achieve enhanced operational efficiency, reduced security risks, and improved decision-making capabilities. The paper concludes that AI-driven cloud ecosystems represent the next evolutionary step in enterprise cybersecurity and data governance frameworks
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: federated intelligence
  topic: keywords
subject: cloud security
  topic: keywords
subject: cyber resilience
  topic: keywords
subject: data governance
  topic: keywords
subject: zero trust architecture
  topic: keywords
subject: federated learning
  topic: keywords
subject: multi-cloud systems
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: privacy engineering
  topic: keywords
subject: enterprise security
  topic: keywords
subject: intelligent orchestration
  topic: keywords
subject: predictive analytics
  topic: keywords
creator: Peter Rasmus Jonathan
rights: Copyright (c) 2024
date: 2024-09-07
publication date: 2024-09-07
starting page: 11278
ending page: 11288
doi: 10.15662/IJRPETM.2024.0705015

Lineage, Traceability, and Reproducibility as Reliability Requirements in Enterprise AI Systems
type: item
description: Artificial intelligence is being applied to key business and compliance choices by more systems in the enterprise. One of the most common systems is concerned with the accuracy of the model and does not factor in the reliability aspect, like the lineage or traceability, or reproducibility. In this paper, we obtain these three aspects as fundamental reliability expectations of enterprise AI. The study was a real enterprise AI applied in 12 months with a before and after quantitative design. Lineage coverage, version control and reproducibility controls were introduced thus, the lineage coverage rose to 0.91 and the success of reproducibility rose to 92% after these tools were applied on structured lineage. Rapid time to incident investigation was less by 66%, audit preparation was also less by 62% and compliance findings were also less by 75%. Monte Carlo simulation also indicated that the risk variability was smaller when the lineage controls had been incorporated. This observation is in full agreement with the results that indicated that integrating lineage, traceability, and reproducibility into AI platforms enhances reliability, audit readiness, and trust in AI results.
subject: Articles
  topic: section
subject: Enterprise Artificial Intelligence
  topic: keywords
subject: Traceability
  topic: keywords
subject: , Data Lineage
  topic: keywords
subject: Reproducibility
  topic: keywords
subject: Data Governance
  topic: keywords
subject: AI Reliability
  topic: keywords
subject: Model Versioning
  topic: keywords
creator: Divya Bonthala
rights: Copyright (c) 2026
date: 2026-04-13
publication date: 2026-04-13
starting page: 641
ending page: 650
doi: 10.15662/w14s7t29

Federated Learning Architecture for Privacy Preserving Healthcare Data Governance and Scalable Cloud Native Intelligent Systems
type: item
description: The rapid digitization of healthcare systems has led to an unprecedented growth in sensitive patient data, raising critical concerns regarding privacy, security, and governance. Traditional centralized machine learning approaches require data aggregation, which increases the risk of data breaches and regulatory non-compliance. This paper proposes a federated learning-based architecture designed to enable privacy-preserving healthcare data governance while supporting scalable intelligent systems within cloud-native platforms. The proposed framework allows distributed healthcare institutions to collaboratively train machine learning models without sharing raw data, thereby ensuring data sovereignty and compliance with regulations such as HIPAA and GDPR. Leveraging cloud-native technologies such as containerization, microservices, and orchestration, the system ensures scalability, resilience, and efficient resource utilization. The architecture integrates secure aggregation protocols, differential privacy mechanisms, and blockchain-based audit trails to enhance trust and transparency. Additionally, it supports real-time analytics and decision-making capabilities critical for modern healthcare applications. Experimental analysis demonstrates improved model accuracy, reduced latency, and enhanced data privacy compared to traditional approaches. This research highlights the potential of federated learning combined with cloud-native infrastructure to transform healthcare data governance into a secure, scalable, and intelligent ecosystem.
subject: Articles
  topic: section
subject: Federated learning
  topic: keywords
subject: healthcare data governance
  topic: keywords
subject: privacy preservation
  topic: keywords
subject: privacy preservationdistributed machine learning
  topic: keywords
subject: data security
  topic: keywords
subject: differential privacy
  topic: keywords
subject: blockchain
  topic: keywords
subject: scalability
  topic: keywords
subject: intelligent systems
  topic: keywords
creator: Amit Kumar Jain
rights: Copyright (c) 2024
date: 2024-09-11
publication date: 2024-09-11
starting page: 11268
ending page: 11277
doi: 10.15662/IJRPETM.2024.0705013

Building Intelligent Systems from Data Silos Enabling Trustworthy AI and Cloud Ecosystems for Modern Healthcare
type: item
description: Modern healthcare systems generate vast amounts of heterogeneous data across hospitals, laboratories, wearable devices, and electronic health records. However, these data remain fragmented in silos, limiting their potential to support intelligent decision-making. This paper explores the development of intelligent systems that integrate siloed healthcare data to enable trustworthy artificial intelligence (AI) and cloud-driven ecosystems. It highlights the challenges associated with data fragmentation, interoperability, privacy, and security while emphasizing the importance of federated architectures and standardized data exchange protocols. The study proposes a framework that combines cloud computing, AI models, and privacy-preserving techniques such as federated learning and differential privacy to facilitate secure data sharing without compromising patient confidentiality. By leveraging scalable cloud infrastructure, healthcare providers can achieve real-time analytics, predictive modeling, and personalized treatment recommendations. Furthermore, the research underscores the role of trustworthiness in AI systems, including transparency, fairness, accountability, and robustness. The integration of explainable AI mechanisms ensures that clinical decisions are interpretable and reliable. The findings suggest that overcoming data silos can significantly enhance healthcare delivery, improve patient outcomes, and foster innovation. Ultimately, the study advocates for a collaborative, secure, and interoperable healthcare ecosystem powered by trustworthy AI and cloud technologies
subject: Articles
  topic: section
subject: Healthcare data silos
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Cloud computing
  topic: keywords
subject: Federated learning
  topic: keywords
subject: Interoperability
  topic: keywords
subject: Trustworthy AI
  topic: keywords
subject: Data privacy
  topic: keywords
subject: Predictive analytics
  topic: keywords
subject: Electronic health records
  topic: keywords
subject: Digital health ecosystems
  topic: keywords
creator: Abhay Katiyar
rights: Copyright (c) 2026
date: 2026-02-10
publication date: 2026-02-10
starting page: 198
ending page: 207
doi: 10.15662/IJRPETM.2026.0901025

A Decentralized Security Model for Preventing Data Breaches in Distributed Environments
type: item
description: Distributed computing environments became an important component of the contemporary business, cloud computing, Internet of Things and data-centric apps. However, they rest on the huge number of interconnected nodes, which can lead to an increased susceptibility to unauthorized access, insider attacks, poor authentication and huge data breaches. The research study proposes a non-centralized method of security against security breaches in distributed environments due to the reduction in the use of a central authority and increase in trust, transparency and resiliency among the networked systems. The proposed model enhances the security in the various levels through the identity verification, distributed access control, encrypted data sharing, node level authentication and real-time anomaly detection. In the model where security choices are decentralized, then the impact of a compromised node and single points of failures becomes lesser. Cryptographic hashing ensures data integrity and traceability, and access permissions are automated using smart contracts. It is also possible to use this framework of continuously monitoring the activity to forecast the potential suspicious behaviour and also transferring the undesired data prior to the breach happening. The article suggests that decentralization of security architecture can offer superior confidentiality, integrity, availability and accountability of the uncommon distributed infrastructures. Overall, the proposed model offers an extensible and adaptable system of protection of sensitive information on the cloud, edge, and enterprise networks. In its own way, it contributes to the body of knowledge regarding cybersecurity in the sense that it provides a practical security model, which can be compatible with the growing need of trustless, transparent, and breach-resilience distributed systems.
subject: Articles
  topic: section
subject: Decentralized security
  topic: keywords
subject: data breach prevention
  topic: keywords
subject: distributed environments
  topic: keywords
subject: blockchain security
  topic: keywords
subject: access control
  topic: keywords
subject: encryption
  topic: keywords
subject: anomaly detection
  topic: keywords
creator: Rakesh Kumar Mali
rights: Copyright (c) 2024
date: 2024-01-17
publication date: 2024-01-17
starting page: 9989
ending page: 9999
doi: 10.15662/IJRPETM.2024.0701011

A Scalable Microservice Framework for Multi-Modal Logistics Route Optimization
type: item
description: In this paper, I am going to present a scalable logistics route planning microservice software that could be implemented to optimize the supply chain and ensure that it is more efficient, flexible, and resilient. The pressure to identify efficient and sustainable logistic operations in fast, cost-effective and environmentally-safe freight transportation, and the need of faster, agile and multi-modal route planning, implies it. The classic centralised route planning lacks the ability to address the dynamical, large, as well as multi-modal transportation system. We decentralise also the logistics (microservices) such as the data collection, prediction, assigning routes and assigning vehicles, monitoring the traffic conditions, cost and analytics. It offers real time connection to any kind of transport network (road, railway, air and sea) that is scalable and resilient to faults. Both the data sources and the intelligent decision-making models utilise data in real-time; the data sources gather and capture real-time data to optimise dynamically and identify the optimal alternative path to identify the cost-efficient paths. The systems are also capable of interconnecting with other systems like the warehouse management systems, fleet management systems and the enterprise resource planning systems. The needed scale and a range of logistics use-cases are provided with the help of containers and API-based communication and distributed model. In the paper, the benefits of the innovations in route planning, computational issues and sustainable logistics based on microservices are outlined. Plainly, it is by the framework that the logistics companies will have a treasure trove and scalable solution to the logistic and supply chain giving the revitalization to the efficiency and precision as well as decision making capacities of the multi-mode transport.
subject: Articles
  topic: section
subject: Microservices
  topic: keywords
subject: Multi-modal logistics
  topic: keywords
subject: Route optimization
  topic: keywords
subject: Supply chain management
  topic: keywords
subject: Scalable architecture
  topic: keywords
subject: Real-time data analytics
  topic: keywords
subject: Intelligent transportation systems
  topic: keywords
creator: Rakesh Kumar Mali
rights: Copyright (c) 2023
date: 2023-03-08
publication date: 2023-03-08
starting page: 8382
ending page: 8391
doi: 10.15662/IJRPETM.2023.0602003

Smart Artificial Intelligence Framework for Cloud Centric Systems and Continuous Threat Intelligence Evolution
type: item
description: Cloud-centric systems have become the backbone of modern digital infrastructure, enabling scalable, flexible, and cost-efficient computing environments. However, the widespread adoption of cloud technologies has introduced complex cybersecurity challenges, including data breaches, misconfigurations, insider threats, and advanced persistent attacks. This research proposes a smart artificial intelligence (AI) framework designed to enhance security in cloud-centric systems through continuous threat intelligence evolution. The framework integrates machine learning, deep learning, and adaptive analytics to monitor cloud environments, detect anomalies, and predict potential threats in real time. By leveraging dynamic data streams from cloud platforms, the system continuously learns from new attack patterns and updates its threat intelligence models. The proposed approach emphasizes automation, scalability, and proactive defense mechanisms, enabling rapid response to emerging cyber risks. Additionally, the framework incorporates secure data handling, governance, and compliance measures to ensure data integrity and privacy. Experimental evaluation demonstrates improved detection accuracy, reduced response latency, and enhanced adaptability compared to traditional security systems. The study concludes that integrating AI with cloud security infrastructure provides a robust and intelligent solution for safeguarding cloud-based systems against evolving cyber threats.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Threat Intelligence
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Adaptive Systems
  topic: keywords
creator: Rajabhushanam .C
rights: Copyright (c) 2023
date: 2023-12-12
publication date: 2023-12-12
starting page: 9831
ending page: 9840
doi: 10.15662/IJRPETM.2023.0606025

AI-Enabled Financial Data Governance Using a Secure Cloud-Based Framework for Enterprise Compliance and Automation
type: item
description: The rapid growth of financial data across enterprises has intensified the need for robust governance frameworks that ensure security, compliance, and operational efficiency. This paper proposes an AI-enabled financial data governance model integrated within a secure cloud-based infrastructure to address challenges such as regulatory compliance, data integrity, risk management, and automation. By leveraging artificial intelligence techniques—including machine learning, natural language processing, and anomaly detection—the framework enhances data classification, monitoring, and policy enforcement in real time. Cloud computing provides scalability, flexibility, and cost efficiency, enabling organizations to manage vast volumes of financial data while maintaining stringent security protocols. The proposed framework aligns with global compliance standards and regulatory requirements by automating auditing processes, detecting irregularities, and ensuring transparency in financial reporting. Additionally, it reduces manual intervention, thereby minimizing human error and improving decision-making accuracy. This research highlights how integrating AI with cloud-based governance systems can transform enterprise financial data management into a proactive, intelligent, and adaptive process. The study also evaluates the benefits and limitations of the approach, offering insights into future advancements and adoption challenges. Ultimately, this model supports enterprises in achieving secure, compliant, and efficient financial data governance in an increasingly data-driven environment.
subject: Articles
  topic: section
subject: AI governance
  topic: keywords
subject: financial data management
  topic: keywords
subject: cloud computing
  topic: keywords
subject: enterprise compliance
  topic: keywords
subject: automation
  topic: keywords
subject: data security
  topic: keywords
subject: machine learning
  topic: keywords
subject: regulatory compliance
  topic: keywords
subject: data governance framework
  topic: keywords
subject: risk management
  topic: keywords
creator: Osama S. Faragallah
rights: Copyright (c) 2021
date: 2021-09-08
publication date: 2021-09-08
starting page: 5604
ending page: 5613
doi: 10.15662/IJRPETM.2021.0405007

How Servicenow Impacted Accelerating Clinical Trials
type: item
description: The COVID-19 pandemic has highlighted the importance of speed and efficiency in clinical trials, particularly in the life sciences industry. ServiceNow, a digital workflow platform, plays a crucial role in this change by optimizing procedures, boosting communication, and data management. This thesis investigates ServiceNow's impact on clinical trial acceleration, focusing on its features that improve participant engagement, optimize resource allocation, assure regulatory compliance, and harness future technology. ServiceNow automates processes, enabling real-time data access, and centralizes information on participant recruitment, trial methods, and data collection. It also provides analytics for better decision- making and compliance management. Integrating emerging technologies like AI into ServiceNow improves decision-making skills across clinical trial workflows. The thesis emphasizes the necessity of implementing integrated digital solutions to promote operational efficiency, improve patient experiences, and generate improved health outcomes in clinical research.
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: Clinical Research
  topic: keywords
subject: Servicenow Real-Time Data
  topic: keywords
creator: Sravanthi Mallireddy
rights: Copyright (c) 2023
date: 2023-12-13
publication date: 2023-12-13
starting page: 1
ending page: 7
doi: 10.15662/g4p25021

BUSINESS VALUE OF SERVICENOW FOR HEALTH CARE AND EDUCATION SERVICES
type: item
description: Service Now is a disruptive solution for healthcare and education services, offering efficiency, effectiveness, and better outcomes. As a market leader in digital workflow management, it automates administrative activities, centralizes data management, and enhances collaboration among healthcare practitioners. This allows healthcare workers to focus on high-quality care and ensures accurate patient data. In the education industry, ServiceNow automates complex enrollment, course administration, and student assistance processes, lowering response times and freeing up staff time for student engagement. Its analytics capabilities allow educational institutions to monitor performance indicators, enabling data-driven decisions that improve the learning experience.
subject: Articles
  topic: section
subject: Data-Driven
  topic: keywords
subject: Healthcare Practitioners
  topic: keywords
subject: Digital Workflow
  topic: keywords
subject: Education Industry
  topic: keywords
creator: Sravanthi Mallireddy
rights: Copyright (c) 2022
date: 2022-01-11
publication date: 2022-01-11
starting page: 191
ending page: 196
doi: 10.15662/g7jkze85

Servicenow Create Enterprise Workflows for Various Digitalize Business Processes
type: item
description: In recent years, the digital transformation landscape has evolved at an unparalleled rate, with ServiceNow emerging as a critical platform in changing organizational workflow management. This thesis provides a thorough examination of ServiceNow's Enterprise Workflow capabilities and their revolutionary influence on organizational procedures, efficiency, and digital maturity. This study creates new frameworks for understanding and implementing enterprise-wide digital transformation initiatives by doing thorough research and analysis on implementation strategies, case studies, and empirical data. The study shows how ServiceNow's innovative approach to workflow automation, combined with its robust integration capabilities and intelligent process optimization, allows organizations to achieve unprecedented levels of operational excellence while remaining adaptable in an increasingly dynamic business environment
subject: Articles
  topic: section
subject: Digital Transformation
  topic: keywords
subject: Workflow Automation
  topic: keywords
subject: Organizational Procedures and Digital Maturity
  topic: keywords
creator: Sravanthi Mallireddy
rights: Copyright (c) 2024
date: 2024-08-15
publication date: 2024-08-15
starting page: 1
ending page: 6
doi: 10.15662/8raaxh67

PyHadoopLake: A Python-Native Framework for Building Scalable Lakehouse Architectures on Hadoop
type: item
description: This paper presents PyHadoopLake which is a Python-native framework designed to simplify and modernize the construction of scalable lakehouse architectures on top of the Hadoop distributed file system. The proposed framework integrates extract transform and load operations directly within a Python-driven pipeline while leveraging the distributed processing capabilities of Hadoop. The methodology focuses on combining PySpark with HDFS and Delta Lake table formats to deliver a unified and reproducible data engineering environment. Experimental results demonstrate meaningful improvements in pipeline throughput and data processing latency when compared with traditional Hadoop ETL approaches. The framework offers a practical and extensible solution for organizations seeking to modernize their data infrastructure without abandoning their existing Hadoop investments.
subject: Articles
  topic: section
subject: Data Lakehouse
  topic: keywords
subject: Hadoop
  topic: keywords
subject: Python
  topic: keywords
subject: ETL Pipeline
  topic: keywords
subject: PySpark
  topic: keywords
subject: Distributed Computing
  topic: keywords
subject: HDFS
  topic: keywords
creator: Sunil Kumar Mudusu
rights: Copyright (c) 2022
date: 2022-09-14
publication date: 2022-09-14
starting page: 7449
ending page: 7452
doi: 10.15662/IJRPETM.2022.0505007

Designing Self Adaptive Cloud and AI Platforms for High Performance Secure and Intelligent Systems
type: item
description: The rapid evolution of cloud computing and artificial intelligence has created the need for platforms that are not only scalable but also capable of adapting dynamically to changing workloads, security threats, and performance requirements. This paper explores the design of self-adaptive cloud and AI platforms that integrate intelligent decision-making mechanisms to optimize system performance, enhance security, and ensure resilience. These platforms leverage machine learning, real-time monitoring, and automated orchestration to continuously analyze system behavior and make autonomous adjustments. Key design principles include elasticity, fault tolerance, predictive analytics, and zero-trust security models. The study highlights how adaptive systems can improve resource utilization, reduce latency, and proactively mitigate cyber threats. Furthermore, it discusses architectural frameworks that combine distributed computing, edge intelligence, and hybrid cloud environments. By incorporating self-learning capabilities, these platforms can evolve over time, responding effectively to emerging challenges and workload variability. The paper concludes by emphasizing the importance of integrating AI-driven automation with cloud infrastructure to build intelligent systems that are efficient, secure, and capable of sustaining high performance in dynamic environments.
subject: Articles
  topic: section
subject: Self-adaptive systems
  topic: keywords
subject: cloud computing
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: high performance computing
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: autonomous systems
  topic: keywords
subject: machine learning
  topic: keywords
subject: distributed systems
  topic: keywords
subject: intelligent infrastructure
  topic: keywords
subject: scalability
  topic: keywords
creator: Saraswathi M
rights: Copyright (c) 2026
date: 2026-04-22
publication date: 2026-04-22
starting page: 632
ending page: 640
doi: 10.15662/IJRPETM.2026.0902018

BUILDING SECURE MULTI-CLOUD INFRASTRUCTURE FOR MISSION-CRITICAL ENTERPRISE WORKLOADS
type: item
description: The rapid evolution of enterprise IT landscapes has driven organizations toward adopting multi-cloud strategies to enhance scalability, resilience, and operational agility. However, managing mission-critical workloads across heterogeneous cloud environments introduces significant challenges in security, governance, interoperability, and performance consistency. This paper presents a comprehensive, vendor-neutral framework for building secure multi-cloud infrastructure tailored for mission-critical enterprise applications. It explores architectural principles, zero-trust security models, identity federation, data protection mechanisms, and policy-driven automation required to ensure confidentiality, integrity, and availability across cloud platforms. The study further examines cross-cloud networking, workload portability, disaster recovery strategies, and compliance alignment in regulated industries. Emphasis is placed on integrating cloud-native and hybrid security controls, including encryption, micro- segmentation, and continuous monitoring using AI-driven threat detection systems. Additionally, the paper outlines best practices for leveraging Infrastructure as Code (IaC), container orchestration, and DevSecOps pipelines to enforce consistent security postures across providers. Through conceptual models, architectural diagrams, and comparative analysis, this research highlights how enterprises can mitigate risks associated with vendor lock-in, misconfigurations, and fragmented visibility. The proposed approach enables organizations to achieve a secure, resilient, and scalable multi-cloud ecosystem capable of supporting mission-critical workloads while maintaining regulatory compliance and operational excellence.
subject: Articles
  topic: section
subject: Multi-Cloud Architecture
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: Mission-Critical Workloads
  topic: keywords
subject: Zero Trust Security
  topic: keywords
subject: Identity and Access Management (IAM)
  topic: keywords
subject: Cloud Governance
  topic: keywords
subject: Data Encryption
  topic: keywords
subject: DevSecOps
  topic: keywords
subject: Infrastructure as Code (IaC)
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: Disaster Recovery
  topic: keywords
subject: Compliance
  topic: keywords
subject: Cloud Interoperability
  topic: keywords
subject: Hybrid Cloud
  topic: keywords
subject: Micro-Segmentation
  topic: keywords
subject: AI-driven Threat Detection
  topic: keywords
creator: Rajesh Adepu
rights: Copyright (c) 2022
date: 2022-09-05
publication date: 2022-09-05
starting page: 14
ending page: 32
doi: 10.15662/gdee5975

AI-ENABLED DIGITAL IDENTITY VERIFICATION FRAMEWORK FOR GOVERNMENT SELF-SERVICE PLATFORMS USING SECURE API AND CLOUD INTEGRATION
type: item
description: Digital government initiatives increasingly rely on self-service platforms to deliver citizen services efficiently, securely, and at scale. However, ensuring accurate and secure identity verification remains a major challenge due to rising cyber threats, identity fraud, and the complexity of integrating multiple government databases. This paper proposes an Digital government initiatives increasingly rely on self-service platforms to deliver citizen services efficiently, securely, and at scale. However, ensuring accurate and secure identity verification remains a major challenge due to rising cyber threats, identity fraud, and the complexity of integrating multiple government databases. This paper proposes an AI-enabled digital identity verification framework designed for government self-service platforms that leverages secure API architectures, machine learning-based identity validation, and cloud-native integration models. The framework combines biometric verification, document authentication, and behavioral analytics to provide a multi-layered identity assurance mechanism. The proposed architecture utilizes artificial intelligence techniques such as facial recognition, anomaly detection, and document classification to automate identity verification processes while maintaining compliance with government security and privacy regulations. Secure API gateways and microservice-based integration enable seamless connectivity between government databases, authentication providers, and cloud-based identity services. Cloud infrastructure further supports scalability, high availability, and real-time processing capabilities required for large-scale citizen service platforms. Additionally, the study analyzes key design principles including data privacy protection, interoperability, fraud detection, and system resilience. Conceptual system models, workflow diagrams, and performance considerations are presented to demonstrate how the framework improves verification accuracy, re…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Digital Identity Verification
  topic: keywords
subject: Government Self-Service Platforms
  topic: keywords
subject: Secure APIs
  topic: keywords
subject: Cloud Integration
  topic: keywords
subject: Identity Authentication
  topic: keywords
subject: Biometric Verification
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: Digital Governance
  topic: keywords
creator: Ganesh Adepu
rights: Copyright (c) 2021
date: 2021-01-21
publication date: 2021-01-21
starting page: 160
ending page: 176
doi: 10.15662/acvda274

Governance Led Security Architecture in Large Scale Enterprise Systems
type: item
description: This paper studies governance-led security architecture in large-scale enterprise systems using a quantitative approach. It determines the impact of governance mechanisms like security review boards (mean 4.1), architectural guardrails (3.8), compliance enforcement (3.9) and approval workflows (3.5) in security outcomes in distributed environments. Findings indicate that the high governance minimizes security incidents from 45 incidents to 12 incidents every year and minimizes the compliance violation from 39 to 8. Average resolution time is increased from 5.2 hours from 18.4. There are strong, negative relationships (-0.78) between governance maturity and incidents. Results indicate that there is a necessity to have balanced automated governance structures.
subject: Articles
  topic: section
subject: Governance-led security architecture
  topic: keywords
subject: cloud-native computing
  topic: keywords
subject: enterprise systems
  topic: keywords
subject: microservices security
  topic: keywords
creator: Vasudevan Subramani
rights: Copyright (c) 2023
date: 2023-08-03
publication date: 2023-08-03
starting page: 9037
ending page: 9045
doi: 10.15662/IJRPETM.2023.0604009

Integrating Machine Learning with Business Rule Management Systems for Adaptive Enterprise.
type: item
description: Modern enterprises operate in increasingly volatile, uncertain, complex, and ambiguous (VUCA) environments, necessitating adaptive decision-making mechanisms that can dynamically respond to changing business contexts. Business Rule Management Systems (BRMS) have traditionally enabled organizations to formalize and automate decision logic; however, their deterministic nature limits adaptability. Conversely, Machine Learning (ML) provides predictive and adaptive capabilities but often lacks transparency, interpretability, and governance. This study proposes a hybrid framework integrating ML with BRMS to enable adaptive enterprise decision-making. Using a design science research methodology, the paper develops and evaluates a multi-layered architecture combining predictive analytics with rule-based governance. A detailed healthcare fraud detection case study demonstrates the effectiveness of the approach. The findings indicate that hybrid systems significantly enhance decision accuracy, agility, and compliance, while mitigating risks associated with opaque AI models.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: BRMS
  topic: keywords
subject: Decision Intelligence
  topic: keywords
subject: Adaptive Enterprises
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Rule Engines
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Decision Automation
  topic: keywords
creator: Mallikarjun Bellundagi
rights: Copyright (c) 2023
date: 2023-01-10
publication date: 2023-01-10
starting page: 8023
ending page: 8039
doi: 10.15662/IJRPETM.2023.0601004

A Multi-Layer AI-Driven Decision Intelligence Framework for Enterprise and Healthcare System
type: item
description: This paper proposes a comprehensive multi-layer AI-driven decision intelligence framework designed for enterprise and healthcare systems, addressing critical limitations in existing AI implementations where data, models, and decision-making processes often operate in isolation, thereby restricting scalability, explainability, and real-world applicability. The proposed framework introduces five interconnected layers data, intelligence, decision, application, and analytics each performing a distinct role in transforming raw data into actionable insights. By integrating machine learning models, rule-based reasoning, and advanced analytics, the framework ensures adaptive, accurate, and transparent decision-making. A healthcare case scenario is utilized to validate the framework, demonstrating improvements in clinical decision support, resource allocation, and response time. The evaluation results indicate enhanced decision accuracy, reduced processing time, and improved operational efficiency compared to traditional approaches. This study contributes a scalable and domain-independent architecture that bridges the gap between AI models and actionable decisions, supporting future intelligent systems with better interpretability and performance.
subject: Articles
  topic: section
subject: AI-driven decision intelligence
  topic: keywords
subject: multi-layer architecture
  topic: keywords
subject: healthcare analytics
  topic: keywords
subject: enterprise AI systems
  topic: keywords
subject: explainable AI (XAI)
  topic: keywords
subject: predictive modeling
  topic: keywords
subject: rule-based decision systems
  topic: keywords
creator: Mallikarjun Bellundagi
rights: Copyright (c) 2024
date: 2024-11-11
publication date: 2024-11-11
starting page: 11679
ending page: 11687
doi: 10.15662/IJRPETM.2024.0706027

Building Foundational Data Integrity in Enterprise Retail Systems: A Structured Approach to Early-Stage Data Governance
type: item
description: In modern enterprise retail environments, data serves as the foundational asset that drives operational efficiency, customer engagement, supply chain optimization, and strategic decision-making. However, many retail organizations face persistent challenges related to inconsistent, incomplete, and fragmented data across multiple systems such as point-of-sale platforms, inventory management systems, supplier databases, and customer relationship management applications. These challenges often originate during the early stages of system implementation when structured data governance frameworks are not yet established. Without foundational data integrity mechanisms, organizations risk propagating data errors across interconnected systems, resulting in inaccurate analytics, operational inefficiencies, and compromised business insights. This paper explores a structured approach to establishing foundational data integrity in enterprise retail systems through early-stage data governance practices. The study examines key principles of data governance, including data ownership models, standardized data definitions, validation frameworks, metadata management, and automated quality monitoring mechanisms. It further analyzes how structured governance policies implemented during the initial stages of system deployment can significantly reduce long-term data inconsistencies and improve cross-platform interoperability. The paper proposes a governance-driven architectural model that integrates data quality controls, master data management strategies, and governance workflows into enterprise retail ecosystems. Through conceptual frameworks, architectural diagrams, and governance lifecycle models, the research highlights best practices for aligning technical data management processes with organizational governance policies. Additionally, the paper discusses how emerging technologies such as automation, metadata catalogs, and rule-based validation engines can support proactive data inte…
subject: Articles
  topic: section
subject: Enterprise Retail Systems
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Data Integrity
  topic: keywords
subject: Master Data Management (MDM)
  topic: keywords
subject: Data Quality Management
  topic: keywords
subject: Retail Data Architecture
  topic: keywords
subject: Metadata Management
  topic: keywords
subject: Data Lifecycle Governance
  topic: keywords
subject: Data Validation Frameworks
  topic: keywords
subject: Enterprise Data Management
  topic: keywords
creator: V Balamuralidhar Sarabu
rights: Copyright (c) 2018
date: 2018-08-20
publication date: 2018-08-20
starting page: 2457
ending page: 2465
doi: 10.15662/8jrgxd68

ENSURING DATA INTEGRITY AND OPERATIONAL CONTINUITY IN MISSION-CRITICAL ENTERPRISE PLATFORM MIGRATIONS: AN ARCHITECTURAL FRAMEWORK
type: item
description: Enterprise platform migrations have become a strategic necessity for organizations seeking to modernize legacy infrastructures, improve scalability, and support evolving digital services. However, migrating mission-critical enterprise platforms such as enterprise resource planning (ERP), financial management systems, and large-scale data warehouses poses significant risks related to data integrity, operational disruption, and system interoperability. Even minor inconsistencies during migration can lead to cascading failures, financial inaccuracies, regulatory compliance issues, and prolonged service outages. As a result, organizations require structured architectural frameworks that ensure both data reliability and uninterrupted business operations throughout the migration lifecycle. This paper presents an architectural framework designed to ensure data integrity and operational continuity during mission-critical enterprise platform migrations. The proposed framework integrates principles of phased migration strategies, automated validation mechanisms, transactional consistency models, and resilient system architecture patterns. The study examines how layered migration architectures, incorporating data replication, metadata-driven synchronization, and rollback mechanisms, can minimize risks associated with large-scale system transitions. Additionally, the framework highlights the role of governance models, migration orchestration tools, and monitoring systems in maintaining visibility and control over complex multi-system environments. The paper further analyzes key architectural components including source system abstraction, transformation pipelines, synchronization controllers, and integrity verification layers. By applying these mechanisms within controlled migration phases such as preparation, parallel operations, validation, and final cutover organizations can significantly reduce the probability of data corruption and service interruptions. Operational contin…
subject: Articles
  topic: section
subject: Enterprise Platform Migration
  topic: keywords
subject: Data Integrity
  topic: keywords
subject: Operational Continuity
  topic: keywords
subject: Enterprise Architecture
  topic: keywords
subject: System Modernization
  topic: keywords
subject: Data Synchronization
  topic: keywords
subject: Migration Frameworks
  topic: keywords
subject: Cloud Migration
  topic: keywords
subject: Data Consistency
  topic: keywords
subject: Mission-Critical Systems
  topic: keywords
creator: V Balamuralidhar Sarabu
rights: Copyright (c) 2026
date: 2026-01-05
publication date: 2026-01-05
starting page: 1
ending page: 18
doi: 10.15662/kejz5474