International Journal of Computer Technology and Electronics Communication
type: channel
language: en
publication name: International Journal of Computer Technology and Electronics Communication
items: Enhancing Federated Cloud with Explainable Generative Artificial Intelligence for Enterprise Data Modernization and Governance
items: Implementing Agentic AI Enabled Multi-Cloud Enterprise Platforms for Intelligent Business Process Automation
items: Modernizing Explainable AI Frameworks for Distributed Cloud Environments and Transparent Enterprise Decision Automation
items: Leveraging Model Context Protocol for Secure Cloud-Based Agentic Enterprise Integration and API Interoperability
items: Modernizing Enterprise Data Platforms via Cloud Computing and Generative Artificial Intelligence with LLMOps
items: Optimizing Enterprise Application Integration using Machine Learning Powered API Lifecycle Management for Cloud Platforms
items: Optimizing Decision Systems through Predictive Analytics for Enterprise Intelligent Cloud Platforms and API Management
items: RELIABLE ENTERPRISE DATA EXCHANGE THROUGH EVENT-DRIVEN SYNCHRONIZATION AND INCREMENTAL PROCESSING
items: Leveraging AI-Enabled Cloud-Native Frameworks for Secure Resilient Enterprise Computing and Cyber Intelligence
items: Design of Intelligent Enterprise Platforms for Artificial Intelligence and Cloud Computing with Intelligent Security and Data Engineering
items: Secure Enterprise Modernization through AI Powered Microservices Hybrid Cloud and Intelligent Regulatory Governance
items: Converging Intelligent Cloud Computing with Enterprise Platforms and Artificial Intelligence Using Predictive Analytics
items: Modernizing Enterprise Systems Using Generative AI with Cloud Native Computing and Leadership Development
items: Next Generation Enterprise Platform for SAP and Azure and Oracle and AWS with Autonomous Infrastructure Intelligence
items: Smart Enterprise Intelligence using AI Cloud Platforms Blockchain and Predictive Analytics
items: Designing Enterprise Innovation through Cloud Native Platforms for Autonomous AI and Intelligent Supply Chains
items: Self Governing Artificial Intelligence Framework for Enterprise Operations Compliance Monitoring and Predictive Risk Analytics
items: A Federated AI-Enabled Enterprise DevSecOps Framework for Autonomous Cloud Security Orchestration and Continuous Compliance
items: Artificial Intelligence Enabled Cloud Native Platforms for Intelligent Enterprise Integration and Secure Business Operations
items: Modernizing Enterprise Service Desk and EUC Operations with AI-Powered Automation
items: Automation-Led Digital Transformation for Banking, Insurance, and Manufacturing Enterprises
items: Integrated Cloud and Artificial Intelligence Solutions for Smart Governance Fraud Detection and Digital Service Delivery
items: Intelligent Cloud Native Enterprise Platforms for AI-Driven Cybersecurity SAP Digital Innovation and Autonomous DevOps
items: Self-Optimizing Pipelines ML Systems That Tune Themselves in Production
items: Agentic Orchestration and Integration of PBX and SaaS CRM Platforms
items: Modernizing Enterprise Integration Architecture: A Case Study of Oracle Cloud Integration
items: Vendor Dependency to Enterprise Sovereignty: A Phased Migration Approach for Enterprise Applications
items: Automated Patient Quality Data Flow for CMS Reporting Accuracy
items: High-Availability Network Diagnostics and Configuration Platform for Real-Time Financial Service Delivery
items: Enterprise Asset Management Digitalization for Multi-Site Pharmaceutical Manufacturing a Transatlantic Oracle eAM Implementation

Enhancing Federated Cloud with Explainable Generative Artificial Intelligence for Enterprise Data Modernization and Governance
type: item
description: Modern enterprises are increasingly adopting federated multi-cloud environments to prevent vendor lock-in, maintain localized data sovereignty, and leverage specialized infrastructure across geographic regions. However, managing distributed data estates across heterogeneous cloud architectures introduces severe operational bottlenecks in data discovery, schema standardization, compliance enforcement, and cross-silo lineage tracking. Traditional centralized data governance models introduce performance friction and struggle to scale alongside high-velocity, unstructured data assets. This paper presents a novel framework integrating Explainable Generative Artificial Intelligence (xGenAI) into federated cloud architectures to automate enterprise data modernization and continuous governance. By embedding localized retrieval-augmented generative models and automated ontology mappers at distributed cloud nodes, the framework dynamically generates unified semantic metadata, maps cross-cloud lineage, and enforces fine-grained access policies. To ensure transparency and regulatory compliance, the system incorporates Shapley Additive exPlanations (SHAP) and attention-map attribution mechanisms, offering human-interpretable reasoning for automated classification, policy decisions, and synthetic data generation. Empirical evaluation across a simulated multi-cloud ecosystem demonstrates significant improvements in metadata auto-cataloging throughput, drastic reductions in policy audit latency, and enhanced explainability metrics over conventional governance toolchains.
subject: Articles
  topic: section
subject: Federated Cloud
  topic: keywords
subject: Explainable Generative AI
  topic: keywords
subject: Enterprise Data Modernization
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Retrieval-Augmented Generation
  topic: keywords
subject: Feature Attribution
  topic: keywords
subject: Cross-Cloud Lineage
  topic: keywords
creator: Maheshwari Muthusamy
rights: Copyright (c) 2025
date: 2025-10-10
publication date: 2025-10-10
starting page: 11586
ending page: 11591
doi: 10.15680/IJCTECE.2025.0805036

Implementing Agentic AI Enabled Multi-Cloud Enterprise Platforms for Intelligent Business Process Automation
type: item
description: Modern enterprise environments are undergoing a paradigm shift from rigid, rule-based process automation to dynamic, cognitive orchestration driven by Agentic Artificial Intelligence (AI). Traditional Robotic Process Automation (RPA) and legacy cloud architectures struggle with unstructured data, unexpected workflow exceptions, and multi-cloud fragmentation. This paper introduces a comprehensive framework for implementing Agentic AI across distributed, multi-cloud enterprise ecosystems to achieve end-to-end Intelligent Business Process Automation (IBPA). Utilizing multi-agent orchestration topologies, autonomous tool invocation, dynamic task decomposition, and real-time retrieval-augmented context engineering, the proposed architecture abstracts underlying multi-cloud complexity—spanning AWS, Microsoft Azure, Google Cloud Platform, and private edge nodes. We evaluate the implementation using a prototype deployed across hybrid enterprise environments handling complex financial, procurement, and IT operations workflows. The results demonstrate an 81% reduction in manual process intervention, a 4.5× acceleration in end-to-end workflow completion speeds, and an exceptional 99.2% operational resilience rate against cross-cloud system failures. Furthermore, the study presents an in-depth analysis of governance mechanisms, zero-trust cryptographic boundary enforcement, and Human-in-the-Loop (HITL) exception handling required to operate non-deterministic autonomous agents safely within regulated global enterprises.
subject: Articles
  topic: section
subject: Agentic AI
  topic: keywords
subject: Multi-Cloud Architecture
  topic: keywords
subject: Intelligent Business Process Automation
  topic: keywords
subject: Multi-Agent Systems
  topic: keywords
subject: Enterprise Governance
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Retrieval-Augmented Generation
  topic: keywords
subject: Human-in-the-Loop.
  topic: keywords
creator: Dr Himanshu Maniar
rights: Copyright (c) 2025
date: 2025-09-11
publication date: 2025-09-11
starting page: 11576
ending page: 11585
doi: 10.15680/IJCTECE.2025.0805035

Modernizing Explainable AI Frameworks for Distributed Cloud Environments and Transparent Enterprise Decision Automation
type: item
description: The rapid adoption of artificial intelligence (AI) within distributed cloud environments has transformed enterprise decision-making by enabling automated, scalable, and data-driven processes. However, the increasing complexity of AI models, particularly deep learning and hybrid machine learning systems, has created significant challenges regarding transparency, accountability, and trust. Modern enterprises require explainable artificial intelligence (XAI) frameworks that can operate effectively across decentralized cloud infrastructures while providing understandable insights into automated decisions. This essay explores the modernization of XAI frameworks designed for distributed cloud environments and their role in achieving transparent enterprise decision automation. The study examines emerging approaches such as federated explainability, interpretable machine learning, explainability-as-a-service, privacy-preserving explanation methods, and cloud-native AI governance mechanisms. It analyzes existing research challenges related to scalability, model complexity, data heterogeneity, regulatory compliance, and real-time explanation generation. A research methodology based on a mixed-method analytical approach is proposed, integrating systematic literature analysis, framework evaluation, architectural assessment, and comparative investigation of modern XAI techniques. The research emphasizes the importance of developing adaptive, secure, and context-aware explainability frameworks capable of supporting responsible AI adoption. Modernized XAI systems can enhance organizational trust, improve regulatory compliance, and enable transparent enterprise automation while maintaining the performance advantages of distributed cloud-based artificial intelligence
subject: Articles
  topic: section
subject: Explainable Artificial Intelligence
  topic: keywords
subject: Distributed Cloud Computing
  topic: keywords
subject: Transparent Decision Automation
  topic: keywords
subject: Machine Learning Interpretability
  topic: keywords
subject: AI Governance
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Cloud-Native AI
  topic: keywords
subject: Responsible Artificial Intelligence
  topic: keywords
subject: Enterprise Automation
  topic: keywords
subject: Model Transparency
  topic: keywords
creator: Mohammed Zackriah
rights: Copyright (c) 2026
date: 2026-07-17
publication date: 2026-07-17
starting page: 1521
ending page: 1529
doi: 10.15680/IJCTECE.2026.0904003

Leveraging Model Context Protocol for Secure Cloud-Based Agentic Enterprise Integration and API Interoperability
type: item
description: The rapid evolution of agentic artificial intelligence (AI) has introduced complex challenges in enterprise integration, particularly concerning secure data orchestration, API interoperability, and context maintenance across fragmented cloud ecosystems. This paper examines the deployment of the Model Context Protocol (MCP) as a standardized framework for establishing secure, bi-directional communication between LLM-based autonomous agents and enterprise data sources. By formalizing context exchange protocols, MCP mitigates common vulnerabilities associated with traditional webhook architectures and ad-hoc API wrappers, such as credential exposure, loose access controls, and contextual drift. We propose a robust architectural blueprint that integrates MCP within cloud-native environments, leveraging zero-trust network access (ZTNA), fine-grained tokenization, and dynamic schema mapping to ensure high-fidelity API interoperability. Through a comprehensive evaluation of data processing efficiency, latency overhead, and boundary security compliance, this research demonstrates how MCP-driven agentic workflows minimize semantic loss while enforcing strict enterprise data governance boundaries. Ultimately, this study provides actionable insights for enterprise architects aiming to scale secure, autonomous AI systems that interact seamlessly with legacy enterprise resource planning (ERP) platforms, customer relationship management (CRM) databases, and distributed microservices architectures without compromising structural data sovereignty.
subject: Articles
  topic: section
subject: Model Context Protocol
  topic: keywords
subject: Agentic AI
  topic: keywords
subject: Enterprise Integration
  topic: keywords
subject: API Interoperability
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: Zero Trust Architecture
  topic: keywords
subject: Semantic Web
  topic: keywords
creator: Thomas Friess
rights: Copyright (c) 2025
date: 2025-09-17
publication date: 2025-09-17
starting page: 11567
ending page: 11575
doi: 10.15680/IJCTECE.2025.0805034

Modernizing Enterprise Data Platforms via Cloud Computing and Generative Artificial Intelligence with LLMOps
type: item
description: The rapid evolution of digital transformation has increased the demand for scalable, intelligent, and adaptive enterprise data platforms capable of supporting complex business operations. Traditional data infrastructures often struggle with growing data volumes, diverse data sources, real-time processing requirements, and the increasing need for advanced analytics. Cloud computing has emerged as a foundation for modern enterprise data platforms by providing flexible infrastructure, scalable storage, distributed computing capabilities, and advanced data management services. The integration of Generative Artificial Intelligence (Generative AI) further enhances these platforms by enabling automated content generation, intelligent data analysis, natural language interaction, and advanced decision support. However, deploying and managing large language models (LLMs) in enterprise environments introduces challenges related to model lifecycle management, scalability, governance, security, and operational efficiency. LLMOps addresses these challenges by applying DevOps principles to the development, deployment, monitoring, and optimization of AI models. This research explores the modernization of enterprise data platforms through the combination of cloud computing, Generative AI, and LLMOps practices. The study examines how cloud-native architectures and AI-driven capabilities improve data accessibility, operational efficiency, automation, and business intelligence. The proposed methodology evaluates architectural approaches, implementation strategies, and governance frameworks required to build secure, scalable, and intelligent enterprise data ecosystems
subject: Articles
  topic: section
subject: Cloud computing
  topic: keywords
subject: enterprise data platforms
  topic: keywords
subject: Generative Artificial Intelligence
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: LLMOps
  topic: keywords
subject: MLOps
  topic: keywords
subject: cloud-native architecture
  topic: keywords
subject: data modernization
  topic: keywords
subject: AI automation
  topic: keywords
subject: intelligent analytics
  topic: keywords
subject: data governance
  topic: keywords
subject: scalable infrastructure
  topic: keywords
creator: Michele Mosca
rights: Copyright (c) 2024
date: 2024-12-12
publication date: 2024-12-12
starting page: 9987
ending page: 9996
doi: 10.15680/IJCTECE.2024.0706032

Optimizing Enterprise Application Integration using Machine Learning Powered API Lifecycle Management for Cloud Platforms
type: item
description: Enterprise application integration has become a critical capability for organizations seeking to connect diverse software systems, cloud services, databases, and digital platforms within increasingly complex technology environments. Application Programming Interfaces (APIs) serve as the foundation of modern integration strategies by enabling communication, data exchange, and interoperability among distributed applications. However, the rapid growth of APIs across cloud platforms has created significant challenges related to lifecycle management, security, scalability, performance optimization, and governance. This study examines the role of machine learning-powered API lifecycle management in optimizing enterprise application integration for cloud platforms. Machine learning techniques provide intelligent capabilities for API discovery, usage prediction, anomaly detection, automated testing, performance monitoring, security enhancement, and lifecycle optimization. By analyzing operational data and API behavior patterns, machine learning models enable proactive decision-making and automated management throughout API development, deployment, operation, and retirement phases. The research adopts a qualitative methodology based on an extensive review of academic literature, industry frameworks, and cloud integration practices to explore current approaches and emerging trends. The findings indicate that integrating machine learning with API lifecycle management improves integration efficiency, reduces operational complexity, strengthens security, enhances service reliability, and supports scalable cloud-native architectures. The study concludes that intelligent API lifecycle management represents a strategic approach for enterprises seeking adaptive, efficient, and resilient application integration capabilities in modern cloud environments
subject: Articles
  topic: section
subject: Enterprise Application Integration
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: API Lifecycle Management
  topic: keywords
subject: Cloud Platforms
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: API Governance
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Intelligent Automation
  topic: keywords
subject: Microservices
  topic: keywords
subject: Microservices,
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Application Programming Interfaces
  topic: keywords
subject: Cloud Integration
  topic: keywords
subject: Enterprise Architecture
  topic: keywords
subject: API Security
  topic: keywords
creator: Matz Andersson
rights: Copyright (c) 2026
date: 2026-07-17
publication date: 2026-07-17
starting page: 1511
ending page: 1520
doi: 10.15680/IJCTECE.2026.0904002

Optimizing Decision Systems through Predictive Analytics for Enterprise Intelligent Cloud Platforms and API Management
type: item
description: Modern corporate environments rely on complex, cloud-native infrastructures where localized information delays can compromise competitive advantage. This paper introduces an optimized, multi-layered architectural model for Decision Support Systems (DSS) embedded within Enterprise Intelligent Cloud Platforms, coordinated through advanced Application Programming Interface (API) management layers. Traditional enterprise decision engines frequently operate on rigid, retrospective batch processing, failing to proactively respond to erratic demand spikes, localized service degradations, and backend hardware faults. By synthesizing real-time predictive analytics with elastic cloud resources, the proposed framework transitions enterprise decision systems into proactive operational systems. The intelligence layer leverages specialized gradient-boosted trees and deep sequence-to-sequence neural network architectures to continuous analyze multi-variant telemetry data, forecasting resource constraints and transaction volumes before they disrupt operations. Operating as the essential communication system, an optimized API management structure uses predictive inferences to adjust traffic routing, dynamically update web application firewall policies, and manage rate-limiting rules. The result is a highly adaptive, resilient cloud ecosystem that minimizes human operational overhead, ensures continuous system availability, and increases institutional decision-making velocities across highly distributed global enterprise networks.
subject: Articles
  topic: section
subject: Decision Support Systems
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: API Management
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Intelligent Architecture
  topic: keywords
subject: Traffic Orchestration
  topic: keywords
creator: Oscar Ricardo López
rights: Copyright (c) 2024
date: 2024-11-15
publication date: 2024-11-15
starting page: 9979
ending page: 9986
doi: 10.15680/IJCTECE.2024.0706031

RELIABLE ENTERPRISE DATA EXCHANGE THROUGH EVENT-DRIVEN SYNCHRONIZATION AND INCREMENTAL PROCESSING
type: item
description: Modern enterprises increasingly depend on continuous data exchange across heterogeneous applications, cloud platforms, legacy systems, and partner ecosystems. Traditional batch-oriented integration approaches often struggle to satisfy modern business expectations for low-latency synchronization, operational resilience, scalability, and data consistency. Event-driven synchronization combined with incremental data processing has emerged as a practical architectural paradigm that enables organizations to exchange information efficiently while minimizing network utilization, processing overhead, and synchronization delays. Rather than transferring complete datasets during every integration cycle, incremental processing focuses only on newly created, modified, or deleted records, significantly improving system performance and reducing infrastructure costs This article presents a generalized enterprise framework for implementing reliable data exchange through event-driven synchronization and incremental processing. The discussion covers architectural principles, synchronization models, event lifecycle management, change data identification strategies, messaging infrastructures, consistency mechanisms, monitoring approaches, and operational governance. It further examines techniques for ensuring fault tolerance, scalability, security, and data integrity across distributed enterprise environments. The paper also highlights implementation challenges such as duplicate event handling, out-of-order delivery, schema evolution, network failures, and recovery strategies, together with practical mitigation techniques. In addition, the article introduces a reference architecture suitable for hybrid and multi-cloud enterprise ecosystems and compares event-driven synchronization with conventional batch integration methods. Various deployment considerations, performance optimization strategies, and operational best practices are discussed to assist architects and engineering teams in d…
subject: Articles
  topic: section
subject: Event-Driven Architecture (EDA)
  topic: keywords
subject: Enterprise Data Exchange
  topic: keywords
subject: Incremental Data Processing
  topic: keywords
subject: Change Data Capture (CDC)
  topic: keywords
subject: Event Synchronization
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: Enterprise Integration
  topic: keywords
subject: Data Consistency
  topic: keywords
subject: Event Streaming
  topic: keywords
subject: Message-Oriented Middleware
  topic: keywords
subject: Asynchronous Communication
  topic: keywords
subject: Data Replication
  topic: keywords
subject: Hybrid Cloud Integration
  topic: keywords
subject: Microservices
  topic: keywords
subject: Event Processing
  topic: keywords
subject: Data Synchronization Framework
  topic: keywords
subject: Reliability Engineering
  topic: keywords
subject: Scalability
  topic: keywords
subject: Fault Tolerance
  topic: keywords
subject: Enterprise Architecture
  topic: keywords
creator: Vivekananda Reddy Polamreddy
rights: Copyright (c) 2025
date: 2025-05-10
publication date: 2025-05-10
starting page: 10776
ending page: 10780
doi: 10.15680/k84z7f74

Leveraging AI-Enabled Cloud-Native Frameworks for Secure Resilient Enterprise Computing and Cyber Intelligence
type: item
description: The rapid evolution of digital enterprises has created an increasing demand for intelligent, scalable, and resilient technology ecosystems capable of addressing complex cybersecurity challenges, operational disruptions, and dynamic business requirements. AI-enabled cloud-native frameworks represent a transformative approach that integrates artificial intelligence, machine learning, automation, containerization, microservices, serverless architectures, and advanced security mechanisms to enhance enterprise agility and resilience. These frameworks enable organizations to develop adaptive infrastructures that can continuously monitor threats, predict failures, optimize resource utilization, and support rapid recovery from disruptions. This research explores the role of AI-driven cloud-native technologies in establishing secure and resilient enterprise environments. It examines how artificial intelligence enhances cloud-native security through automated threat detection, behavioral analytics, anomaly identification, and intelligent incident response while cloud-native principles improve scalability, portability, and operational efficiency. The study investigates architectural approaches, implementation strategies, security practices, and governance models required for successful adoption. A qualitative research methodology based on extensive analysis of academic literature, industry frameworks, and enterprise technology practices is applied to evaluate the effectiveness of AI-enabled cloud-native ecosystems. The findings indicate that organizations integrating AI capabilities with cloud-native architectures can achieve improved cybersecurity posture, operational continuity, and business adaptability. However, challenges related to data governance, model transparency, integration complexity, and regulatory compliance must be effectively managed. The research highlights the importance of adopting holistic frameworks that combine artificial intelligence, cloud engineering,…
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: cloud-native computing
  topic: keywords
subject: enterprise resilience
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: machine learning
  topic: keywords
subject: microservices
  topic: keywords
subject: containerization
  topic: keywords
subject: DevSecOps
  topic: keywords
subject: intelligent automation
  topic: keywords
subject: cloud security
  topic: keywords
subject: adaptive infrastructure
  topic: keywords
creator: Sami Honkonen
rights: Copyright (c) 2023
date: 2023-12-10
publication date: 2023-12-10
starting page: 8146
ending page: 8158
doi: 10.15680/IJCTECE.2023.0606033

Design of Intelligent Enterprise Platforms for Artificial Intelligence and Cloud Computing with Intelligent Security and Data Engineering
type: item
description: Modern digital corporations require complex IT ecosystems that can turn massive volumes of data into actionable insights while guaranteeing structural stability and security. This paper introduces an advanced architectural framework for designing intelligent enterprise platforms that natively blend Artificial Intelligence (AI) and cloud computing with autonomous security and robust data engineering. Traditional enterprise infrastructures often treat processing engines, cloud storage, and security tools as isolated silos, which results in latency bottlenecks, broken data lineages, and vulnerabilities to advanced cyber attacks. By creating a unified, data-mesh-driven ecosystem hosted on multi-cloud environments, this design framework leverages automated metadata fabrics and machine learning to build data pipelines that are fully AI-ready. Furthermore, we explore the integration of intelligent security mechanisms, combining continuous behavioral analytics, automated threat modeling, and self-healing zero-trust architectures to defend against malicious data injection and zero-day exploits. This study provides a step-by-step methodology for orchestrating these distinct domains into a single, cohesive compute platform. Ultimately, the proposed architectural design shows that combining intelligent data processing with autonomous security models significantly improves enterprise scalability, lowers operating costs, and ensures long-term regulatory compliance.
subject: Articles
  topic: section
subject: Intelligent Enterprise Platforms
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Intelligent Security
  topic: keywords
subject: Data Engineering
  topic: keywords
subject: Data Mesh Architecture
  topic: keywords
creator: Dr.K.Ravikumar
rights: Copyright (c) 2026
date: 2026-03-30
publication date: 2026-03-30
starting page: 662
ending page: 668
doi: 10.15680/IJCTECE.2026.0902025

Secure Enterprise Modernization through AI Powered Microservices Hybrid Cloud and Intelligent Regulatory Governance
type: item
description: Enterprise modernization has become a critical strategic initiative as organizations seek to improve agility, scalability, security, and regulatory alignment in increasingly complex digital environments. Traditional enterprise systems often face limitations due to monolithic architectures, fragmented infrastructures, manual operations, and difficulties in maintaining compliance with evolving regulations. This research presents a Secure Enterprise Modernization framework that integrates Artificial Intelligence (AI)-powered microservices, hybrid cloud computing, and intelligent regulatory governance to enable adaptive, secure, and efficient digital transformation. The proposed framework leverages AI-driven analytics, automated service management, cloud-native architectures, and continuous governance mechanisms to improve enterprise operations. AI-powered microservices enhance application flexibility by enabling intelligent automation, predictive decision-making, and independent service scalability. Hybrid cloud integration provides optimized workload distribution, improved resource utilization, and enhanced business continuity across distributed environments. Intelligent regulatory governance introduces automated compliance monitoring, risk assessment, security policy enforcement, and continuous auditing capabilities. The framework addresses challenges associated with cybersecurity threats, cloud complexity, and regulatory requirements by combining intelligent technologies with enterprise architecture principles. The research demonstrates that integrating AI, microservices, hybrid cloud, and governance capabilities can support secure enterprise modernization while improving operational efficiency, resilience, and compliance readiness. The proposed approach provides a foundation for future intelligent enterprises capable of autonomous management, proactive security protection, and continuous innovation
subject: Articles
  topic: section
subject: Secure Enterprise Modernization
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Microservices Architecture
  topic: keywords
subject: Hybrid Cloud Computing
  topic: keywords
subject: Intelligent Governance
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
subject: Cloud-Native Systems
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Cybersecurity Automation
  topic: keywords
creator: Antonio Brogi
rights: Copyright (c) 2022
date: 2022-10-15
publication date: 2022-10-15
starting page: 5781
ending page: 5788
doi: 10.15680/IJCTECE.2022.0505007

Converging Intelligent Cloud Computing with Enterprise Platforms and Artificial Intelligence Using Predictive Analytics
type: item
description: The rapid evolution of corporate digital infrastructure requires a structural convergence between scalable computing, hardened corporate defenses, and predictive analytics. This paper details a unified framework for converging Intelligent Cloud Computing with Secure Enterprise Platforms and Artificial Intelligence Innovation Using Predictive Analytics. Traditional corporate architectures frequently treat data analysis, security infrastructure, and cloud storage as isolated, independent systems. This structural fragmentation introduces significant processing delays and exposes enterprises to sophisticated cyber threats. By embedding advanced predictive analytics engines directly into the core virtualization and security layers of intelligent cloud infrastructures, this framework transforms enterprise systems from defensive repositories into proactive intelligence engines. The architecture utilizes machine learning models to forecast operational bottlenecks, anticipate sophisticated cyber attacks before they penetrate the perimeter, and optimize cloud resource distribution in real time. Through an integration of zero-trust verification frameworks, automated predictive models, and high-throughput data processing networks, this study outlines how large organizations can achieve continuous operational resilience. Ultimately, this research provides a definitive blueprint for enterprise architects and chief information officers looking to minimize structural complexity, eliminate operational vulnerabilities, and drive sustainable corporate innovation using predictive data insights.
subject: Articles
  topic: section
subject: Intelligent Cloud Computing
  topic: keywords
subject: Secure Enterprise Platforms
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: AI Innovation
  topic: keywords
subject: Zero-Trust Architecture
  topic: keywords
subject: Proactive Cyber Defense
  topic: keywords
creator: R Archana
rights: Copyright (c) 2025
date: 2025-12-17
publication date: 2025-12-17
starting page: 11944
ending page: 11951
doi: 10.15680/IJCTECE.2025.0806039

Modernizing Enterprise Systems Using Generative AI with Cloud Native Computing and Leadership Development
type: item
description: Modernizing legacy enterprise systems is a critical imperative for organizations seeking to maintain competitive advantage in a volatile digital economy. This paper explores a triadic framework for enterprise transformation that integrates Generative Artificial Intelligence (GenAI), Cloud Native Computing, and targeted Leadership Development. While cloud-native architectures provide the scalable, resilient, and elastic infrastructure necessary for modern operations, GenAI introduces unprecedented capabilities for automated code migration, intelligent data synthesis, and hyper-personalized customer experiences. However, technology alone is insufficient; systemic modernization requires a parallel evolution in organizational culture and leadership capabilities. This study investigates how leaders can effectively manage the socio-technical friction inherent in rapid technological transitions, foster psychological safety, and cultivate an algorithmic mindset across teams. By analyzing the intersection of these three domains, we propose an integrated paradigm for sustainable modernization. The findings indicate that organizations aligning infrastructure upgrades with AI-driven automation and adaptive leadership methodologies achieve significantly higher deployment velocities, lower operational risks, and enhanced long-term agility compared to those pursuing purely technical roadmaps. Ultimately, this comprehensive approach ensures that enterprise modernization delivers both immediate engineering efficiencies and enduring strategic value.
subject: Articles
  topic: section
subject: Generative AI
  topic: keywords
subject: Cloud Native Computing
  topic: keywords
subject: Enterprise Modernization
  topic: keywords
subject: Leadership Development
  topic: keywords
subject: Socio-Technical Transformation
  topic: keywords
subject: DevSecOps
  topic: keywords
creator: Dr S Saravana Kumar
rights: Copyright (c) 2023
date: 2023-12-12
publication date: 2023-12-12
starting page: 8137
ending page: 8145
doi: 10.15680/IJCTECE.2023.0606032

Next Generation Enterprise Platform for SAP and Azure and Oracle and AWS with Autonomous Infrastructure Intelligence
type: item
description: The rapid evolution of enterprise technology ecosystems has created the need for intelligent platforms capable of integrating complex business applications, cloud infrastructures, enterprise databases, and autonomous operational capabilities. Traditional enterprise architectures often rely on fragmented systems that limit scalability, interoperability, and real-time decision-making. This research explores a next-generation enterprise platform that integrates SAP enterprise applications, Microsoft Azure cloud services, Oracle technologies, Amazon Web Services (AWS), and autonomous infrastructure intelligence to create a unified digital ecosystem. The proposed platform leverages artificial intelligence, machine learning, cloud computing, automation, predictive analytics, and intelligent orchestration to enhance enterprise agility, operational efficiency, cybersecurity, and business intelligence. Autonomous infrastructure intelligence enables self-monitoring, self-optimization, predictive maintenance, automated resource allocation, and intelligent incident response across hybrid and multi-cloud environments. Integration among enterprise resource planning systems, cloud platforms, databases, analytics solutions, and automation frameworks supports seamless data exchange and strategic decision-making. This study adopts a qualitative research methodology based on systematic literature analysis of enterprise architecture models, cloud computing frameworks, AI-driven infrastructure management approaches, and digital transformation strategies. The findings demonstrate that next-generation enterprise platforms can improve scalability, resilience, governance, and operational intelligence by combining enterprise software ecosystems with autonomous technologies. The research provides a conceptual foundation for organizations seeking adaptive, secure, and intelligent enterprise architectures for future digital transformation
subject: Articles
  topic: section
subject: Next Generation Enterprise Platform
  topic: keywords
subject: SAP
  topic: keywords
subject: Microsoft Azure
  topic: keywords
subject: Oracle
  topic: keywords
subject: Amazon Web Services
  topic: keywords
subject: Autonomous Infrastructure Intelligence
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Hybrid Cloud
  topic: keywords
subject: Multi-Cloud Architecture
  topic: keywords
subject: Enterprise Automation
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Intelligent Operations
  topic: keywords
subject: Enterprise Architecture
  topic: keywords
creator: Javier Ramirez
rights: Copyright (c) 2024
date: 2024-05-16
publication date: 2024-05-16
starting page: 8851
ending page: 8861
doi: 10.15680/IJCTECE.2024.0703011

Smart Enterprise Intelligence using AI Cloud Platforms Blockchain and Predictive Analytics
type: item
description: The rapid evolution of digital technologies has transformed traditional enterprises into intelligent, data-driven ecosystems capable of making autonomous decisions and responding dynamically to changing market conditions. Smart Enterprise Intelligence integrates artificial intelligence (AI), cloud computing platforms, blockchain technology, and predictive analytics to enhance operational efficiency, innovation, security, and strategic decision-making. AI enables organizations to analyze complex datasets, automate business processes, and generate intelligent insights, while cloud platforms provide scalable infrastructure for storing, processing, and managing large volumes of enterprise information. Blockchain contributes decentralized trust, transparency, and secure data exchange across organizational networks, reducing risks associated with fraud and unauthorized access. Predictive analytics applies advanced statistical methods and machine learning algorithms to forecast future trends, customer behavior, operational challenges, and business opportunities. This research explores the combined influence of these technologies in developing intelligent enterprise frameworks that support sustainable growth and competitive advantage. The study examines technological integration, organizational transformation, data-driven management practices, and implementation challenges. A comprehensive research methodology based on qualitative and analytical approaches is adopted to evaluate how AI-driven cloud ecosystems, blockchain-enabled security mechanisms, and predictive intelligence contribute to modern enterprise performance. The findings emphasize that strategic adoption of integrated digital technologies is essential for organizations seeking agility, resilience, and innovation in an increasingly competitive global environment.
subject: Articles
  topic: section
subject: Smart enterprise intelligence
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: cloud computing
  topic: keywords
subject: blockchain technology
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: machine learning
  topic: keywords
subject: digital transformation
  topic: keywords
subject: business intelligence
  topic: keywords
subject: data analytics
  topic: keywords
subject: enterprise innovation
  topic: keywords
subject: intelligent systems
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: automation
  topic: keywords
subject: decision-making
  topic: keywords
subject: Industry 4.0
  topic: keywords
creator: Hiroshi Lockheimer
rights: Copyright (c) 2026
date: 2026-07-08
publication date: 2026-07-08
starting page: 1501
ending page: 1510
doi: 10.15680/IJCTECE.2026.0904001

Designing Enterprise Innovation through Cloud Native Platforms for Autonomous AI and Intelligent Supply Chains
type: item
description: Enterprise innovation is undergoing a fundamental transformation driven by cloud-native platforms and the emergence of autonomous artificial intelligence systems. Organizations are increasingly shifting from monolithic IT architectures to distributed, microservices-based ecosystems that enable rapid scalability, continuous delivery, and real-time intelligence. In parallel, supply chains are evolving into highly interconnected, data-driven networks that require adaptive decision-making capabilities and resilience against disruptions. This paper explores how cloud-native platforms serve as foundational enablers for enterprise innovation, particularly in the context of autonomous AI systems and intelligent supply chain management. It examines how containerization, Kubernetes orchestration, serverless computing, and event-driven architectures collectively support the development of self-managing AI agents capable of optimizing logistics, forecasting demand, and improving operational efficiency. The discussion also highlights the convergence of AI, edge computing, and cloud ecosystems in enabling real-time analytics and autonomous decision loops across supply networks. Furthermore, the study considers challenges such as data governance, interoperability, security, and ethical implications of autonomous decision systems. By synthesizing existing literature and conceptual frameworks, the paper demonstrates that cloud-native paradigms not only enhance technological agility but also redefine enterprise competitiveness in the era of intelligent automation. Ultimately, it argues that organizations that strategically integrate autonomous AI within cloud-native infrastructures will achieve superior adaptability, cost efficiency, and innovation capacity in increasingly complex global supply chains
subject: Articles
  topic: section
subject: Cloud-native platforms
  topic: keywords
subject: autonomous AI
  topic: keywords
subject: intelligent supply chains
  topic: keywords
subject: microservices
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: serverless computing
  topic: keywords
subject: digital transformation
  topic: keywords
subject: enterprise innovation
  topic: keywords
subject: edge computing
  topic: keywords
creator: Vignesh Dhanabal
rights: Copyright (c) 2026
date: 2026-06-11
publication date: 2026-06-11
starting page: 1119
ending page: 1126
doi: 10.15680/IJCTECE.2026.0903014

Self Governing Artificial Intelligence Framework for Enterprise Operations Compliance Monitoring and Predictive Risk Analytics
type: item
description: The increasing complexity of enterprise operations and regulatory requirements has accelerated the adoption of Artificial Intelligence (AI) as a strategic tool for governance, compliance monitoring, and predictive risk management. A self-governing Artificial Intelligence framework integrates intelligent automation, machine learning, predictive analytics, and real-time monitoring to enable autonomous decision support while ensuring regulatory compliance and operational efficiency. Unlike conventional enterprise systems that depend heavily on manual intervention, self-governing AI continuously learns from organizational data, detects anomalies, predicts potential risks, and recommends corrective actions with minimal human supervision. Such frameworks combine advanced data analytics, business intelligence, cloud computing, and governance mechanisms to create adaptive systems capable of responding dynamically to evolving business environments. Compliance monitoring is strengthened through automated policy enforcement, continuous auditing, and intelligent reporting, while predictive risk analytics enhances organizational resilience by identifying financial, operational, cybersecurity, and regulatory risks before they materialize. This study explores the design and significance of self-governing AI frameworks for modern enterprises, emphasizing their role in improving decision-making, operational transparency, and business sustainability. Furthermore, the paper discusses technological components, governance principles, implementation strategies, and challenges associated with autonomous AI systems. The integration of intelligent governance and predictive analytics is expected to transform enterprise management by promoting efficiency, accountability, innovation, and long-term competitive advantage in digitally driven organizations.
subject: Articles
  topic: section
subject: Self-Governing Artificial Intelligence
  topic: keywords
subject: Enterprise Operations
  topic: keywords
subject: Compliance Monitoring
  topic: keywords
subject: Predictive Risk Analytics
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Intelligent Automation
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Enterprise Governance
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
subject: Risk Management
  topic: keywords
subject: Decision Intelligence
  topic: keywords
subject: Data Analytics
  topic: keywords
creator: Mattias Andersson
rights: Copyright (c) 2022
date: 2022-12-13
publication date: 2022-12-13
starting page: 16265
ending page: 16274
doi: 10.15680/IJCTECE.2022.0506027

A Federated AI-Enabled Enterprise DevSecOps Framework for Autonomous Cloud Security Orchestration and Continuous Compliance
type: item
description: Modern enterprise cloud ecosystems are increasingly distributed, multi-cloud, and edge-integrated, creating significant challenges in maintaining consistent security posture and regulatory compliance. Traditional DevSecOps pipelines, while effective in integrating security into CI/CD workflows, remain largely centralized and reactive, lacking the intelligence and autonomy required for dynamic cloud-native environments. This paper proposes a Federated AI-Enabled Enterprise DevSecOps Framework designed to enable autonomous cloud security orchestration and continuous compliance across heterogeneous infrastructure The framework integrates federated learning, reinforcement learning-based security automation, and policy-as-code mechanisms to ensure decentralized intelligence sharing without compromising data sovereignty. By embedding AI agents across development, security, and operations layers, the system continuously learns from distributed telemetry, detects anomalies in real time, and autonomously remediates threats. Furthermore, compliance is enforced continuously using machine-readable regulatory policies mapped to dynamic cloud workloads The proposed model enhances scalability, reduces mean-time-to-detection (MTTD), and improves compliance accuracy in multi-tenant cloud environments. It also addresses critical challenges such as data privacy, cross-border governance, and adversarial threats in AI-driven security systems. This framework represents a shift from reactive DevSecOps pipelines to proactive, self-healing, and federated security ecosystems
subject: Articles
  topic: section
subject: Federated learning
  topic: keywords
subject: DevSecOps
  topic: keywords
subject: cloud security
  topic: keywords
subject: autonomous orchestration
  topic: keywords
subject: continuous compliance
  topic: keywords
subject: AI security
  topic: keywords
subject: policy-as-code
  topic: keywords
subject: multi-cloud governance
  topic: keywords
subject: reinforcement learning
  topic: keywords
subject: cybersecurity automation
  topic: keywords
creator: Cynthiya Mohan
rights: Copyright (c) 2025
date: 2025-12-09
publication date: 2025-12-09
starting page: 11934
ending page: 11942
doi: 10.15680/IJCTECE.2025.0806038

Artificial Intelligence Enabled Cloud Native Platforms for Intelligent Enterprise Integration and Secure Business Operations
type: item
description: Artificial Intelligence (AI) enabled cloud-native platforms are transforming modern enterprise ecosystems by enabling intelligent integration, automation, and secure business operations across distributed environments. These platforms combine cloud-native principles such as microservices, containers, Kubernetes orchestration, and DevSecOps with AI-driven analytics, predictive modeling, and autonomous decision-making capabilities. The convergence of AI and cloud-native architectures enhances scalability, resilience, and operational efficiency while ensuring real-time responsiveness to dynamic business demands. Enterprises are increasingly adopting these platforms to unify fragmented data systems, streamline workflows, and strengthen cybersecurity postures through intelligent threat detection and automated incident response. This study explores the architectural design, integration mechanisms, and security frameworks underlying AI-enabled cloud-native platforms for enterprise systems. It highlights how machine learning models, intelligent APIs, and event-driven architectures contribute to seamless enterprise integration and operational intelligence. Additionally, the paper investigates how AI enhances cloud security through anomaly detection, identity management, and predictive risk analysis. The findings suggest that AI-driven cloud-native systems significantly improve business agility, reduce operational costs, and enhance decision-making accuracy. However, challenges such as data privacy, model bias, interoperability issues, and system complexity remain critical concerns. The research concludes that AI-enabled cloud-native platforms represent the future of secure, scalable, and intelligent enterprise computing.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Cloud Native Computing
  topic: keywords
subject: Enterprise Integration
  topic: keywords
subject: Microservices
  topic: keywords
subject: Kubernetes
  topic: keywords
subject: DevSecOps
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Intelligent Automation
  topic: keywords
subject: Cloud Security
  topic: keywords
creator: MinSoo Kang
rights: Copyright (c) 2024
date: 2024-10-17
publication date: 2024-10-17
starting page: 9527
ending page: 9534
doi: 10.15680/IJCTECE.2024.0705012

Modernizing Enterprise Service Desk and EUC Operations with AI-Powered Automation
type: item
description: Enterprise Service Desks and End-User Computing (EUC) operations play a critical role in ensuring business continuity, employee productivity, and digital experience. However, traditional IT support models are increasingly challenged by growing ticket volumes, hybrid work environments, complex device ecosystems, and rising user expectations for instant, always-available support. These challenges are driving organizations to modernize service desk and EUC operations through Artificial Intelligence (AI), automation, and data-driven service management practices. This paper presents a comprehensive overview of how AI-powered automation is transforming enterprise service desks and EUC environments. It explores the evolution from reactive, ticket-driven support models to predictive, self-healing, and experience-centric IT operations. The article discusses key enabling technologies including conversational AI, virtual agents, AIOps, robotic process automation (RPA), intelligent knowledge management, and analytics-driven service optimization The study also highlights architectural considerations, implementation strategies, governance models, and measurable business outcomes such as reduced mean time to resolution (MTTR), increased first-contact resolution (FCR), cost optimization, and improved employee digital experience (DEX). Challenges such as data quality, change management, ethical AI usage, and integration with legacy IT service management (ITSM) tools are examined alongside best practices for successful adoption By providing a generalized, vendor-neutral perspective, this article aims to guide enterprises seeking to build a resilient, scalable, and AI-enabled support ecosystem capable of meeting the demands of modern digital workplaces
subject: Articles
  topic: section
subject: AI-Powered IT Support
  topic: keywords
subject: Enterprise Service Desk
  topic: keywords
subject: End-User Computing (EUC)
  topic: keywords
subject: IT Service Management (ITSM)
  topic: keywords
subject: AIOps
  topic: keywords
subject: Robotic Process Automation (RPA)
  topic: keywords
subject: Virtual Agents
  topic: keywords
subject: Digital Employee Experience (DEX)
  topic: keywords
subject: Predictive IT Operations
  topic: keywords
subject: Self-Healing Systems
  topic: keywords
subject: Automation in IT Support
  topic: keywords
subject: Intelligent Knowledge Management
  topic: keywords
creator: Samiuddin Mohammed
rights: Copyright (c) 2023
date: 2023-12-30
publication date: 2023-12-30
starting page: 8133
ending page: 8136
doi: 10.15680/99n7a970

Automation-Led Digital Transformation for Banking, Insurance, and Manufacturing Enterprises
type: item
description: Automation-led digital transformation has become a strategic priority for enterprises operating in banking, insurance, and manufacturing sectors due to increasing operational complexity, customer expectations, regulatory requirements, and competitive market pressures. Organizations are adopting intelligent automation technologies such as robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), cloud computing, intelligent document processing (IDP), digital twins, and predictive analytics to modernize legacy systems and optimize enterprise workflows. These technologies enable organizations to improve operational efficiency, reduce manual intervention, enhance compliance, strengthen cybersecurity, and deliver data-driven decision-making capabilities across distributed business environments In the banking industry, automation supports faster transaction processing, fraud detection, personalized financial services, and digital customer onboarding. Insurance enterprises utilize automation to streamline claims processing, underwriting, policy administration, and risk assessment while improving customer engagement and regulatory compliance. Manufacturing organizations leverage industrial automation, Industrial Internet of Things (IIoT), smart factories, and predictive maintenance to enhance production efficiency, supply chain visibility, and quality assurance. Despite these advantages, enterprises continue to face challenges associated with legacy infrastructure integration, data silos, governance complexities, workforce transformation, cybersecurity risks, and scalability limitations This article presents a generalized and technology-focused analysis of automation-led digital transformation across banking, insurance, and manufacturing industries. It examines the architectural foundations, enabling technologies, automation frameworks, integration strategies, operational benefits, implementation challenges, and future trends influencing enterpr…
subject: Articles
  topic: section
subject: Digital Transformation
  topic: keywords
subject: Intelligent Automation
  topic: keywords
subject: Robotic Process Automation (RPA)
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Machine Learning (ML)
  topic: keywords
subject: Banking Technology
  topic: keywords
subject: Insurance Automation
  topic: keywords
subject: Smart Manufacturing
  topic: keywords
subject: Industrial Internet of Things (IIoT)
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Enterprise Integration
  topic: keywords
subject: Workflow Orchestration
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: API Management
  topic: keywords
subject: Data Analytics
  topic: keywords
subject: Process Optimization
  topic: keywords
subject: Digital Banking
  topic: keywords
subject: Smart Factories
  topic: keywords
subject: Enterprise Modernization
  topic: keywords
creator: Samiuddin Mohammed
rights: Copyright (c) 2025
date: 2025-10-10
publication date: 2025-10-10
starting page: 11563
ending page: 11566
doi: 10.15680/jfk76v44

Integrated Cloud and Artificial Intelligence Solutions for Smart Governance Fraud Detection and Digital Service Delivery
type: item
description: The rapid advancement of cloud computing and artificial intelligence (AI) has transformed the way governments and public organizations deliver digital services, manage resources, and protect critical information assets. Integrated cloud and AI solutions enable scalable, secure, and intelligent platforms that enhance operational efficiency, improve decision-making, and provide citizen-centric services. This paper presents a comprehensive framework for integrating cloud-native technologies with AI-driven analytics to support smart governance, fraud detection, and digital service delivery. The proposed approach combines machine learning, predictive analytics, automated workflow orchestration, and cloud-based data management to identify fraudulent activities, optimize public service operations, and strengthen cybersecurity. AI-powered anomaly detection and risk assessment facilitate proactive monitoring of financial transactions, identity verification, and compliance management, thereby reducing operational risks and enhancing transparency. Furthermore, cloud-native architectures improve scalability, high availability, disaster recovery, and interoperability across government departments and enterprise applications. The framework also incorporates secure data governance, privacy protection, and intelligent automation to ensure regulatory compliance while delivering reliable and responsive digital services. By integrating AI with cloud infrastructure, organizations can accelerate digital transformation, improve service quality, and establish resilient governance ecosystems capable of addressing evolving technological and security challenges. The proposed model provides a practical roadmap for developing secure, intelligent, and sustainable digital platforms that support modern governance and enterprise innovation.
subject: Articles
  topic: section
subject: Cloud Computing
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Smart Governance
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Digital Service Delivery
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Cloud-Native Architecture
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Intelligent Automation
  topic: keywords
subject: Risk Management
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Data Governance
  topic: keywords
subject: High Availability
  topic: keywords
creator: Matteo Collina
rights: Copyright (c) 2026
date: 2026-05-18
publication date: 2026-05-18
starting page: 1000
ending page: 1007
doi: 10.15680/IJCTECE.2026.0903013

Intelligent Cloud Native Enterprise Platforms for AI-Driven Cybersecurity SAP Digital Innovation and Autonomous DevOps
type: item
description: Intelligent Cloud-Native Enterprise Platforms integrating AI-driven cybersecurity, SAP digital innovation, and autonomous DevOps represent a new paradigm in enterprise computing where scalability, intelligence, and security are unified within a single architecture. This study explores how cloud-native microservices, containerized SAP systems, and AI-enabled security and operations frameworks can collectively transform enterprise digital ecosystems. The proposed model leverages artificial intelligence for real-time threat detection, predictive analytics for operational optimization, and autonomous DevOps pipelines for continuous integration and deployment. SAP integration ensures enterprise-wide consistency across finance, supply chain, and business operations, while cloud-native infrastructure enables elastic scalability and high availability. AI-driven cybersecurity strengthens enterprise resilience by enabling anomaly detection, behavioral analytics, and automated incident response. Additionally, autonomous DevOps reduces human intervention in software delivery pipelines through self-healing systems, intelligent monitoring, and predictive maintenance. The findings indicate that combining these technologies significantly enhances operational efficiency, reduces security risks, and accelerates digital transformation. However, challenges such as system complexity, integration overhead, and governance of autonomous systems remain critical considerations. Overall, the study demonstrates that intelligent cloud-native enterprise platforms provide a scalable, secure, and adaptive foundation for next-generation digital enterprises.
subject: Articles
  topic: section
subject: Cloud-native computing
  topic: keywords
subject: SAP integration
  topic: keywords
subject: AI cybersecurity
  topic: keywords
subject: autonomous DevOps
  topic: keywords
subject: intelligent enterprise systems
  topic: keywords
subject: microservices architecture
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: digital transformation
  topic: keywords
subject: zero trust security
  topic: keywords
subject: enterprise automation
  topic: keywords
creator: Marten Mickos
rights: Copyright (c) 2024
date: 2024-11-18
publication date: 2024-11-18
starting page: 9971
ending page: 9978
doi: 10.15680/IJCTECE.2024.0706030

Self-Optimizing Pipelines ML Systems That Tune Themselves in Production
type: item
description: Production machine learning pipelines are typically built, trained, and tested using historical data, then deployed with a fixed set of settings. However, real-world environments are constantly changing. Customer behavior evolves, data patterns shift, and system resources fluctuate. When performance drops, data scientists must manually investigate the problem, retune the model, and redeploy — a process that is slow, expensive, and prone to human error. This article introduces Self-Optimizing Pipelines (SOPs) — machine learning systems that automatically adjust their own configuration while running in production, without any human intervention. An SOP continuously monitors its own performance, detects when the data distribution has changed, searches for better settings (such as hyperparameters, feature selections, and ensemble weights), and safely deploys those improvements. We present a complete system architecture with five interconnected modules. Using a real-world credit card fraud detection dataset with simulated concept drift, we show that an SOP achieves an average F1-score of 0.93 compared to 0.71 for a static pipeline — a 31% improvement. The SOP reduces the need for manual data scientist intervention by 94% and adds only a small increase in processing time (from 12 milliseconds to 16 milliseconds per prediction), which is acceptable for most real-time applications. Our results demonstrate that self-tuning production systems are not only feasible but also stable, maintainable, and ready for deployment with existing open-source tools.
subject: Articles
  topic: section
subject: Self-optimizing pipelines
  topic: keywords
subject: production machine learning
  topic: keywords
subject: online learning
  topic: keywords
subject: concept drift
  topic: keywords
subject: Bayesian optimization
  topic: keywords
subject: MLOps
  topic: keywords
subject: multi-armed bandits
  topic: keywords
subject: automated machine learning
  topic: keywords
subject: adaptive systems
  topic: keywords
creator: Sandeep Sarngadharan
rights: Copyright (c) 2025
date: 2025-03-18
publication date: 2025-03-18
starting page: 10468
ending page: 10476
doi: 10.15680/IJCTECE.2025.0802015

Agentic Orchestration and Integration of PBX and SaaS CRM Platforms
type: item
description: It is a research on the agentic orchestration and integration of PBX (Private Branch Exchange) and SaaS CRM (Customer Relationship Management) platform using a Microservices Architecture with Event-Driven Communication using Apache Kafka as the fundamental tool. The research will work towards integrating the efficiency, scalability and real-time synchronization of these hitherto separate systems. The use of microservices makes every element of the PBX and CRM ecosystem modular, which allows scaling and the optimization of performance separately. The event-driven communication provided by Kafka makes sure there is a smooth flow of data at the lowest latency and increased system responsiveness. In the study, the effectiveness of this method compared to other integration techniques, like Monolithic Architecture and Service-Oriented Architecture (SOA), has been found to perform better in fault tolerance, reduction of operational overhead and processing of real-time events. The results indicate that the methodology offers a solid solution to the integration of complicated enterprise systems that offers a scalable, robust and efficient approach to the contemporary enterprise communication
subject: Articles
  topic: section
subject: Agentic orchestration
  topic: keywords
subject: PBX integration
  topic: keywords
subject: SaaS CRM
  topic: keywords
subject: microservices architecture
  topic: keywords
subject: event-driven communication
  topic: keywords
subject: Apache Kafka
  topic: keywords
subject: system scalability
  topic: keywords
creator: Sai Sidharth Sambasivan Chettiyar
rights: Copyright (c) 2025
date: 2025-03-12
publication date: 2025-03-12
starting page: 10451
ending page: 10467
doi: 10.15680/IJCTECE.2025.0802014

Modernizing Enterprise Integration Architecture: A Case Study of Oracle Cloud Integration
type: item
description: An example of an organization that provides payroll administration services for more than seven hundred forty thousand (740,000) clients is an example of a company implementing Oracle Cloud ERP. Because of the company's very delicate point to point connectivity between Salesforce and Oracle Applications (i.e., the lack of automated data movement), there have been numerous issues/roadblocks regarding data movement between applications such as manual intervention to move data from Salesforce to Oracle Applications due to a lack of automation in moving data between applications, delays in synchronizing financial data for reporting, and high degree of operational risk due to poor data quality across systems. As a means of addressing these issues reported above, this organization embarked on a comprehensive modernization initiative — transitioning from point to point integrations to an API-led integration approach by leveraging Oracle Integration Cloud (OIC) as its integration bus. The resulting outcomes include real-time synchronization of customer records, full automation of procurement workflows, zero data loss across financial data migrations, GDPR-compliant field-level encryption, and a CI/CD pipeline with integrated performance regression testing — all of which were validated through UAT and production cutover. The results of these efforts have resulted in this company being fully compliant with GDPR and having eliminated all forms of lost data and performance regressions. This case study provides a model for other organizations to consider in their efforts to improve their Hybrid Integration Environment.
subject: Articles
  topic: section
subject: Oracle Integration Cloud (OIC)
  topic: keywords
subject: Oracle Cloud ERP
  topic: keywords
subject: Salesforce CRM
  topic: keywords
subject: Procure-to-Pay Automation
  topic: keywords
subject: FBDI Data Migration
  topic: keywords
subject: CI/CD pipeline
  topic: keywords
subject: VBCS
  topic: keywords
subject: GDPR Compliance
  topic: keywords
subject: API-Led Connectivity
  topic: keywords
creator: Sreedhar Chenna
rights: Copyright (c) 2025
date: 2025-06-17
publication date: 2025-06-17
starting page: 10768
ending page: 10775
doi: 10.15680/IJCTECE.2025.0803012

Vendor Dependency to Enterprise Sovereignty: A Phased Migration Approach for Enterprise Applications
type: item
description: Vendor lock-in remains a significant concern for organizations that rely on proprietary cloud platforms and provider-specific services. Such dependencies often result in limited application portability, increased operational costs, and reduced control over enterprise data and infrastructure. Existing techniques primarily focus on multi-cloud strategies and containerization approaches; however, these solutions typically address isolated layers but fail to offer a comprehensive, enterprise-wide migration pathway. To overcome these limitations, this paper proposes a phased migration framework that enables enterprise applications to transition systematically from vendor-locked architectures to enterprise-sovereign environments. The proposed approach integrates vendor dependency assessment, architectural decoupling, adoption of open standards, and hybrid multi-platform deployment to support gradual transformation with minimal operational disruption. Unlike existing methodologies, the framework introduces a structured dependency classification model and a staged migration plan that collectively enhance portability, governance, and technological flexibility. This work demonstrates how organizations can progressively reduce vendor dependence while maintaining system stability, ultimately achieving long-term digital sovereignty and a sustainable enterprise architecture
subject: Articles
  topic: section
subject: Vendor Lock-in
  topic: keywords
subject: Enterprise Sovereignty
  topic: keywords
subject: Cloud Migration
  topic: keywords
subject: Multi-Cloud Architecture
  topic: keywords
subject: Vendor Dependency Management
  topic: keywords
subject: Enterprise Application Modernization
  topic: keywords
subject: and Platform-Independent Architecture
  topic: keywords
creator: Vikram Govindan
rights: Copyright (c) 2025
date: 2025-07-15
publication date: 2025-07-15
starting page: 11176
ending page: 11185
doi: 10.15680/IJCTECE.2025.0804021

Automated Patient Quality Data Flow for CMS Reporting Accuracy
type: item
description: The research focuses on the application of an AI-based eCQM Calculation Engine and real-time data integration to improve the precision and efficiency of CMS reporting. By utilizing the PowerChart of Cerner, the system is able to extract, validate, and calculate clinical quality measures (eCQMs) of Electronic Health Records (EHR) without manually entering data into the system and minimizes human error. Real-time feedback and AI-provided anomaly detection make the data very accurate and reported in time, which is in line with the CMS requirements. The paper indicates how the automation of patient quality data flow provided by the PowerChart system in Cerner enhances interoperability, enhances the workflows in reporting as well as compliance with regulatory considerations. This is further complemented by the scalability of the system, which is applicable in various healthcare environments, both small practices and big hotel systems. Finally, the resulting effect of this strategy is that it does not only increase the accuracy of CMS reporting but also provides improved healthcare decision-making and improved patient care outcomes
subject: Articles
  topic: section
subject: Automated patient data flow
  topic: keywords
subject: CMS reporting
  topic: keywords
subject: eCQM calculation
  topic: keywords
subject: AI integration
  topic: keywords
subject: Cerner PowerChart
  topic: keywords
subject: real-time data validation
  topic: keywords
subject: healthcare interoperability
  topic: keywords
creator: Srikanth Mannem
rights: Copyright (c) 2024
date: 2025-07-10
publication date: 2025-07-10
starting page: 11161
ending page: 11175
doi: 10.15680/IJCTECE.2025.0804020

High-Availability Network Diagnostics and Configuration Platform for Real-Time Financial Service Delivery
type: item
description: This paper will therefore introduce and illustrate how Chkroute, a network fault-tolerant inspection and configuration system, integrated in the WEBIR proxy server of Thomson Reuters impacts its operations. As the so called Performance Remote Configuration Repository, it allows verification and access to the modes of operation across a wide geographical area, where more than 2000 high-frequency price servers and over 200+ global web traffic management services have to be maintained for prompt delivery of financial data in real time. Developed with the help of Core Java, Java Servlets, etc., this system looks at the core bottlenecks in a large financial institution network- configuration drift, manual audit effort, silos of manageability and the slow service adoption timescales. “The current use of the system within the organization has been able to eliminate this negative impact and demonstrates new capabilities because of those changes. Furthermore, this solution has eradicated all the above-mentioned issues and provides new functionality. For example, configuration time is reduced by 40%, error rates are decreased by 83% and the number of server information audits completed simultaneously has increased to 10 t ims. The centralized nature of the platform invalidated any threats arising from the management of a decentralized network, while dramatically shortening the time that was taken to create valid financial data products. Such findings, as revealed in the study, are also relevant when developing high-capacity, high-speed, and latency-sensitive network management systems critical to the existence of any organization.
subject: Articles
  topic: section
subject: Network Diagnostics
  topic: keywords
subject: High-Availability Systems
  topic: keywords
subject: Financial Data Delivery
  topic: keywords
subject: Proxy Configuration
  topic: keywords
subject: Java Servlet Architecture
  topic: keywords
subject: Oracle Database
  topic: keywords
subject: VIP Validation
  topic: keywords
subject: Remote Configuration Repository
  topic: keywords
creator: Sri Pavanendra Gandikota
rights: Copyright (c) 2025
date: 2025-07-15
publication date: 2025-07-15
starting page: 11147
ending page: 11160
doi: 10.15680/IJCTECE.2025.0804019

Enterprise Asset Management Digitalization for Multi-Site Pharmaceutical Manufacturing a Transatlantic Oracle eAM Implementation
type: item
description: Digitalization of enterprise asset management in pharmaceutical manufacturing is a strategic necessity for global companies. A unique case study on Oracle enterprise asset management was implemented in the manufacturing plants of a global pharmaceutical company in both Indianapolis, USA and the UK. The unified digital system established across these two sites provided synchronized schedules for preventative maintenance and spare parts inventory, while also ensuring 21 CFR Part 11 compliance for electronic records. The methodology used in this project included functional architecture design, cross-functional initiation management, and comprehensive change management amongst international sites. The results of this case study demonstrated how data-driven repair vs. replace decisions resulted in significant cost optimizations on both capital and operating expenditures; how there were no longer production shutdowns due to local stock outs; and how all 100% of employees were proficient in complying with electronic record management. This case study adds to the existing body of literature around Pharma 4.0 by offering an applicable framework to multi-site pharmaceutical companies wishing to harmonize asset management principles while remaining compliant on a global scale within their manufacturing operations
subject: Articles
  topic: section
subject: Oracle eAM, enterprise asset management
  topic: keywords
subject: digital transformation
  topic: keywords
subject: pharmaceutical manufacturing
  topic: keywords
subject: 21 CFR Part 11
  topic: keywords
subject: preventative maintenance
  topic: keywords
subject: multi-site manufacturing
  topic: keywords
subject: Pharma 4.0
  topic: keywords
subject: asset governance
  topic: keywords
subject: predictive maintenance
  topic: keywords
creator: Sameel Syed
rights: Copyright (c) 2025
date: 2025-07-07
publication date: 2025-07-07
starting page: 11138
ending page: 11146
doi: 10.15680/IJCTECE.2025.0804018