International Journal of Research and Applied Innovations
type: channel
description: Welcome to IJEETR The International Journal of Engineering & Extended Technologies Research (IJEETR) is a prestigious, peer-reviewed, open-access international journal committed to fostering innovation, collaboration, and excellence in research across the broad spectrum of engineering and technological sciences. In a rapidly evolving world where technology is reshaping every facet of human life, IJEETR strives to be more than just a journal – it is a knowledge hub where scholars, academicians, researchers, and industry practitioners come together to exchange pioneering ideas, transformative research, and ground breaking solutions to global challenges. We believe in bridging the gap between theory and practice, encouraging interdisciplinary collaboration, and ensuring that innovative discoveries reach a worldwide audience with maximum impact. Why Choose IJEETR? Publishing with IJEETR offers you: Rigorous Peer Review Global Reach Timely Publication Indexing & Abstracting Interdisciplinary Collaboration Impact-Oriented Dissemination IJEETR is currently accepting manuscripts for its upcoming issue. We encourage submissions that present: Novel methodologies and experimental results. Innovative frameworks and technological applications. Critical reviews of contemporary research trends. Submit your manuscript to editor@ijeetr.com and be a part of a global network of innovators shaping the future of engineering and technology. Call for Papers Volume 7, Issue 4, July-August 2025 Deadline: 31st August 2025 Notification: 3 to 4 Days Publication: 24 to 36 Hours E-Certificates: 6 Hours Contact Us For manuscript submission, queries, or collaboration opportunities: Email: editor@ijeetr.com Website: www.ijeetr.com Support: +91-9710583466
language: en
publication name: International Journal of Research and Applied Innovations
items: AI-Based Big Data Systems for Real-Time Disaster Response and Resource Allocation
items: AI-Powered Big Data Analytics for Smart Retail Customer Journey Optimization
items: Scalable Cloud Data Processing Models for Digital Banking Transaction Monitoring
items: Cloud-Native Security and Governance Framework for SAP Enterprise Systems Enabled by Machine Learning
items: Secure Enterprise Ecosystems for AI-Enabled Financial Healthcare Intelligence Platforms and Autonomous DevSecOps Automation
items: Liposome-Based Drug Delivery Systems for Targeted Treatment of Breast Cancer
items: Cloud-First AI Security Architecture for Protecting Enterprise Digital Ecosystems and Financial Networks
items: Next Generation AI-Integrated Cloud Systems for Secure Digital Transformation in Healthcare, Finance, and Smart Infrastructure
items: Secure HR Data Exchange between SAP SuccessFactors and Payroll Using AI-Optimized Encryption, Masking, and Data Minimization Controls
items: From Reactive Alerts to Predictive Intelligence: AI-Assisted Monitoring in Modern Cloud Environments
items: Machine Learning-Enabled Predictive Security and Governance Frameworks for SAP-Integrated Cloud-Native Enterprise Systems
items: Intelligent Data Lakehouse and MLOps Pipelines for Scalable Predictive Analytics in Cloud-Based Enterprise Systems
items: Machine Learning–Enabled Enterprise Lakehouse and Multi-Cloud Data Architecture for Scalable Financial and Business Intelligence Systems
items: AI-Augmented Cloud Monitoring Platforms for Predictive Infrastructure Reliability
items: Multi-Agent AI Architectures for Automated Customer Experience Management Platforms
items: AI-Driven Incident Prediction Models for Proactive IT Operations Management
items: Next Generation Intelligent Enterprise Framework Integrating Generative AI IoT Analytics Predictive Security and Scalable Hybrid Cloud Infrastructure
items: Measuring Business Value of End-to-End Quote-to-Cash with SAP CPQ + CRM + ECC/S4HANA
items: Zero Trust AI Architecture for Enterprise Healthcare Risk Governance in Cloud Ecosystems with Secure Data Encryption
items: Architecting Near Real Time Data Integration Pipelines with PowerExchange and IICS Streaming
items: An Apache Spark Driven AI Framework for Enterprise Healthcare Analytics with Genetic Algorithm Optimization and Blockchain Security
items: Cloud-Native AI Pipelines for Real-Time Cyber Threat Intelligence
items: AI-Driven Autonomous Knowledge Assistants for Enterprise IT Helpdesks
items: Cognitive AI for Autonomous Security Operations Hybrid Threat Detection Intrusion Avoidance and SOC Resilience in Cloud-Native Ecosystems
items: Scalable Cloud and AI Integration for Intelligent Transportation Safety Systems
items: Leveraging Cloud-Enabled AI for Intelligent Traffic Prediction and Sustainable Urban Systems
items: Building the Adaptable Enterprise: Trends in Composable and Event-Driven Salesforce Architectures
items: Autonomous AI Agents for Enterprise Workflow Orchestration in HR Platforms
items: Resilient Middleware and API-Driven Interoperable Platforms for Open Banking and Large-Scale Enterprise Integration
items: Integrated Cloud-Native AI and ML Framework for Secure and Compliant Healthcare–Financial Systems

AI-Based Big Data Systems for Real-Time Disaster Response and Resource Allocation
type: item
description: AI-based big data systems support near real-time disaster response and resource allocation. These systems shorten reaction times by leveraging scarce resources (data, bandwidth, processing, and decision-making) more efficiently in disaster management. A layered architecture provides distributed computing, data fusion, and decision management for near real-time deployment. Future implementations will prototype alarm generation using social-media and sensor data. Large-scale disasters place severe and often insurmountable demands on emergency agencies. Under such conditions, resource allocation plays a critical role in minimizing disaster impact, but conventional methods based on historical data often cannot meet real-time requirements. The integration of geographical information with data from social networks, sensor networks, and other sources can facilitate the relevant prediction and decision-making processes, but the available data, computing, and human resources are all severely constrained—with the same scarcity of responses and alerts as for other data types. For these reasons, proposed methods shorten response times by applying AI-based big-data systems.
subject: Articles
  topic: section
subject: Big Data
  topic: keywords
subject: Disaster
  topic: keywords
subject: Streaming Data
  topic: keywords
subject: Real-Time
  topic: keywords
subject: Predictive Modelling
  topic: keywords
subject: Resource Allocation
  topic: keywords
subject: Command-and-Control
  topic: keywords
subject: Decision Management
  topic: keywords
subject: Social and Open Data
  topic: keywords
subject: Sensor Networks
  topic: keywords
subject: Internet of Things
  topic: keywords
creator: Shashikala Valiki
rights: Copyright (c) 2024
date: 2024-12-18
publication date: 2024-12-18
starting page: 11947
ending page: 11962
doi: 10.15662/IJRAI.2024.0706039

AI-Powered Big Data Analytics for Smart Retail Customer Journey Optimization
type: item
description: Intensifying of competition among modern retail companies necessitates a transition from big data to enhanced intelligence through optimization of the still-unknown customer journey by means of artificial intelligence and business analytics. Such a transition calls for a rethinking of conceptual foundations and development of a data ecosystem. Data constitute a fundamental part of modern intelligent systems. The new-age retail environment produces tremendous amounts and varieties of data about customer experiences during their journey, both online and offline. A model-based customer-journey-mapping and touchpoint-optimization technique and its supporting architecture are presented. Other needed technologies encompass a wide spectrum of artificial intelligence techniques for machine learning, deep learning, and natural language processing. Regulatory and ethical aspects of the exploitation of such a data ecosystem support the smart retail business. Customer journey mapping defines all stages of customer experience with a company or brand and includes its online and offline components. Every stage encompasses several touchpoints for customer experience assessment: awareness, consideration, intent, evaluation, purchase, consumption, and advocacy. For a comprehensive customer-journey model, advanced customer experience metrics are required in addition to those for each touchpoint: net promoter score, customer satisfaction score, customer lifetime value, customer engagement score, and customer effort score. All customer-journey metrics determine the optimization of touchpoint impact on profit.
subject: Articles
  topic: section
subject: Retail Analytics
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Customer Behavior
  topic: keywords
subject: Personalization
  topic: keywords
subject: Omnichannel Retail
  topic: keywords
subject: Consumer Insights
  topic: keywords
subject: Digital Retail Transformation
  topic: keywords
subject: Data-Driven Decision Making
  topic: keywords
subject: Customer Engagement
  topic: keywords
subject: Retail Optimization
  topic: keywords
subject: Experience Management
  topic: keywords
subject: Intelligent Retail Systems
  topic: keywords
subject: Consumer Journey Analytics
  topic: keywords
subject: Retail Data Integration
  topic: keywords
subject: AI-driven Marketing
  topic: keywords
subject: Customer Lifetime Value
  topic: keywords
subject: Retail Intelligence Platforms
  topic: keywords
subject: Behavioral Analytics
  topic: keywords
subject: Decision Support Systems
  topic: keywords
subject: Retail Innovation
  topic: keywords
creator: Madhu Sathiri
rights: Copyright (c) 2023
date: 2023-12-13
publication date: 2023-12-13
starting page: 10040
ending page: 10056
doi: 10.15662/IJRAI.2023.0606030

Scalable Cloud Data Processing Models for Digital Banking Transaction Monitoring
type: item
description: Electronic banking offers new opportunities for credit unions and other community financial institutions. By allowing members to bank 24 hours a day from virtually anywhere in the world, the benefits of electronic banking must be weighed against the potential for increased fraud and illicit transactions. Regulatory requirements mandate that as a result of the Money Laundering Control Act of 1986, banks need to have a formal anti-money laundering program that includes procedures to monitor electronic banking activity. Cloud computing technologies provide scalable computing resources at a reduced operational cost, but their use for anti-money laundering systems has not yet been demonstrated. The characteristics of digital banking transactions indicate these systems may benefit from cloud-based resources. Appropriate cloud architectures and data ingestion and transformation methods are proposed to support real-time rule-based compliance system detection and machine-learning-based anomaly detection. Cloud processing scalability creates processing capacity for compliance requirements without negatively impacting the underlying economic advantage. Cloud computing has emerged as a disruptive technology enabling scalable and affordable data processing in a rapidly evolving environment, and organizations have seized the opportunity to move data storage and processing outside the enterprise firewall. Scalable data processing can be a significant advantage for organizations with irregularly occurring, high-volume access patterns. For compliance monitoring and operational reporting, however, regulatory requirements necessitate monitoring and reporting within a defined period. Such rigid time constraints reduce the potential operational cost benefit of cloud-scale processing resources. Digital transaction characteristics suggest compliance monitoring and anomaly detection can be separated and deployed in a hybrid processing model. The rule-based detection models of the operation…
subject: Articles
  topic: section
subject: Digital banking transaction monitoring
  topic: keywords
subject: a cloud data management and processing system model
  topic: keywords
subject: a bank transaction monitoring system that adheres to regulatory requirements
  topic: keywords
subject: cloud processing of banking data
  topic: keywords
subject: banking data digital transformation
  topic: keywords
subject: real-time transaction monitoring of digital banking transactions
  topic: keywords
subject: near real-time monitoring of banking transactions
  topic: keywords
subject: rule-based monitoring of banking transactions
  topic: keywords
subject: machine-learning-based monitoring of banking transactions
  topic: keywords
subject: full-text data storage for banking transactions
  topic: keywords
creator: Anumandla Mukesh
rights: Copyright (c) 2022
date: 2022-12-15
publication date: 2022-12-15
starting page: 8180
ending page: 8196
doi: 10.15662/IJRAI.2022.0506028

Cloud-Native Security and Governance Framework for SAP Enterprise Systems Enabled by Machine Learning
type: item
description: The rapid evolution of digital technologies has significantly transformed the way enterprises operate, manage data, and deliver services. Organizations today generate enormous volumes of data from enterprise resource planning systems, customer platforms, supply chains, Internet of Things devices, and digital business applications. However, many enterprises struggle to integrate these diverse data sources effectively, leading to fragmented systems and inefficient decision-making processes. Artificial Intelligence (AI) combined with cloud-based enterprise platforms offers a promising solution for addressing these challenges. SAP cloud technologies provide an integrated environment that supports intelligent data management, advanced analytics, and scalable digital infrastructures. This research paper presents an AI-driven SAP cloud architecture designed to facilitate intelligent enterprise data integration and enable scalable digital transformation. The proposed architecture combines SAP Business Technology Platform, machine learning services, cloud-native microservices, and automated data pipelines to create an intelligent enterprise ecosystem. The paper discusses the architectural framework, integration strategies, implementation methodology, benefits, and challenges associated with deploying AI-enabled SAP cloud architectures in modern enterprises. The study demonstrates how organizations can leverage AI-powered cloud technologies to improve operational efficiency, enhance real-time decision-making, and achieve sustainable digital transformation.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: SAP Cloud Architecture
  topic: keywords
subject: SAP Business Technology Platform
  topic: keywords
subject: Intelligent Enterprise
  topic: keywords
subject: Enterprise Data Integration
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Enterprise Resource Planning
  topic: keywords
subject: Data Analytics
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Cloud Integration
  topic: keywords
subject: Scalable Architecture
  topic: keywords
subject: Intelligent Automation
  topic: keywords
subject: Big Data
  topic: keywords
subject: Smart Enterprise Systems
  topic: keywords
subject: Digital Innovation
  topic: keywords
creator: Hassan Ahmed Rashid Al-Mazrouei
rights: Copyright (c) 2024
date: 2024-10-17
publication date: 2024-10-17
starting page: 11447
ending page: 11455
doi: 10.15662/IJRAI.2024.0705017

Secure Enterprise Ecosystems for AI-Enabled Financial Healthcare Intelligence Platforms and Autonomous DevSecOps Automation
type: item
description: The transformation of enterprise digital infrastructures through cloud computing has enabled organizations to deliver services more efficiently, scale operations, and integrate advanced analytics. However, this shift also introduces critical challenges in security, compliance, and operational efficiency, especially in industries handling sensitive data, such as finance and healthcare. Traditional security and operations models are insufficient for dynamic cloud environments, where continuous monitoring, rapid threat mitigation, and automated governance are required. This research proposes an AI-enabled secure cloud ecosystem framework for enterprise platforms, financial technologies, and healthcare intelligence, integrating autonomous DevSecOps automation. The proposed architecture leverages artificial intelligence for real-time threat detection, predictive analytics, and intelligent automation of security and operational workflows. Cloud-native microservices, container orchestration, and continuous integration/continuous deployment (CI/CD) pipelines are combined with AI-driven monitoring to enhance enterprise agility and resilience. The framework also incorporates autonomous DevSecOps principles, enabling automated security policy enforcement, vulnerability remediation, and operational scaling without human intervention. By integrating secure cloud-native infrastructure, AI analytics, and autonomous automation, the proposed ecosystem supports financial transaction integrity, healthcare data privacy, and enterprise operational efficiency. The study provides architectural design principles, integration strategies, and evaluation methodologies for implementing AI-driven secure cloud ecosystems that enable scalable, intelligent, and resilient enterprise platforms.
subject: Articles
  topic: section
subject: AI enabled secure cloud ecosystems
  topic: keywords
subject: enterprise platform security
  topic: keywords
subject: financial technology security
  topic: keywords
subject: healthcare intelligence systems
  topic: keywords
subject: autonomous DevSecOps automation
  topic: keywords
subject: cloud ecosystem architecture
  topic: keywords
subject: machine learning cybersecurity
  topic: keywords
subject: zero trust cloud environments
  topic: keywords
subject: intelligent cloud security analytics
  topic: keywords
subject: automated security orchestration
  topic: keywords
subject: predictive cyber threat detection
  topic: keywords
subject: secure digital transformation
  topic: keywords
creator: Federico Becattini
rights: Copyright (c) 2025
date: 2025-11-21
publication date: 2025-11-21
starting page: 13143
ending page: 13151
doi: 10.15662/IJRAI.2025.0806037

Liposome-Based Drug Delivery Systems for Targeted Treatment of Breast Cancer
type: item
description: Liposome-based drug delivery systems have emerged as an effective nanotechnological strategy to overcome these limitations by improving drug solubility, stability, pharmacokinetics, and tumor targeting. Liposomes are biocompatible phospholipid vesicles capable of encapsulating both hydrophilic and hydrophobic anticancer agents, enabling controlled drug release and reduced off-target effects. This review provides a comprehensive overview of liposome-based drug delivery systems for targeted treatment of breast cancer. It discusses breast cancer pathophysiology and molecular targets relevant to liposomal design, followed by an in-depth analysis of liposome structure, formulation strategies, and physicochemical properties influencing therapeutic performance. Passive targeting via the enhanced permeability and retention (EPR) effect, active ligand-mediated targeting, and stimuli-responsive liposomal systems are critically examined. The review further highlights therapeutic applications including liposomal chemotherapeutics, combination drug delivery, and gene/siRNA delivery approaches. In vivo pharmacokinetics, biodistribution, clinical progress of approved liposomal formulations, and ongoing clinical developments are also discussed. Additionally, safety, toxicity, and regulatory considerations are addressed, along with key challenges hindering clinical translation. Finally, emerging trends such as multifunctional liposomes, personalized nanomedicine, and scalable manufacturing strategies are explored. Overall, liposome-based drug delivery systems represent a promising and clinically relevant platform for improving the efficacy and safety of breast cancer therapy.
subject: Articles
  topic: section
subject: Liposomes
  topic: keywords
subject: Breast cancer
  topic: keywords
subject: Targeted drug delivery
  topic: keywords
subject: Nanomedicine
  topic: keywords
subject: Chemotherapy
  topic: keywords
subject: Pharmacokinetics
  topic: keywords
subject: Cancer therapy
  topic: keywords
creator: Palma Robert Amosa Lemi, Gurbachan Lal
creator: Navdeep Singh
rights: Copyright (c) 2026
date: 2026-03-11
publication date: 2026-03-11
starting page: 21
ending page: 29
doi: 10.15662/IJRAI.2026.0902003

Cloud-First AI Security Architecture for Protecting Enterprise Digital Ecosystems and Financial Networks
type: item
description: The rapid digitalization of enterprise ecosystems and financial networks has accelerated the adoption of cloud-first strategies, enabling scalability, flexibility, and operational efficiency. However, this digital transformation also introduces heightened cybersecurity risks, including advanced persistent threats, ransomware attacks, and vulnerabilities in distributed cloud and networked environments. This research proposes a cloud-first AI security architecture designed to protect enterprise digital ecosystems and financial networks through real-time threat detection, adaptive risk mitigation, and intelligent decision-making. The framework integrates artificial intelligence and machine learning models to continuously monitor system activity, network traffic, and user behavior, detecting anomalies and predicting potential attacks. Cloud-native technologies, including containerization, microservices, and orchestration platforms, support scalable deployment, high availability, and resilience. Zero-trust security principles enforce strict identity verification, multi-factor authentication, and behavioral access control across users, devices, and applications. Additionally, the architecture incorporates intelligent data governance to ensure compliance with financial regulations, secure data handling, and privacy protection. By combining AI-driven analytics, cloud-first deployment, and proactive incident response, the framework provides a comprehensive solution for securing complex enterprise infrastructures and financial platforms. The research demonstrates that integrating AI with cloud-first security strategies enhances operational resilience, reduces cyber risk exposure, and strengthens trust in digital financial ecosystems.
subject: Articles
  topic: section
subject: Cloud-first security
  topic: keywords
subject: AI-driven cybersecurity
  topic: keywords
subject: Enterprise digital ecosystems
  topic: keywords
subject: Financial network protection
  topic: keywords
subject: Cloud-native architecture
  topic: keywords
subject: Real-time threat detection
  topic: keywords
subject: Zero-trust access control
  topic: keywords
subject: Intelligent data governance
  topic: keywords
subject: Adaptive risk mitigation
  topic: keywords
subject: Cyber resilience
  topic: keywords
creator: Dr.Vimal Raja Gopinathan
rights: Copyright (c) 2023
date: 2023-11-29
publication date: 2023-11-29
starting page: 10031
ending page: 10039
doi: 10.15662/IJRAI.2023.0606029

Next Generation AI-Integrated Cloud Systems for Secure Digital Transformation in Healthcare, Finance, and Smart Infrastructure
type: item
description: The rapid digital transformation of critical sectors such as healthcare, finance, and smart infrastructure has significantly increased the demand for secure, scalable, and intelligent cloud computing systems. Traditional security frameworks are insufficient to handle evolving cyber threats, data privacy requirements, and the complexity of distributed cloud-native architectures. This paper proposes a next generation AI-integrated cloud system architecture designed to support secure digital transformation across critical industries. The proposed framework integrates artificial intelligence, machine learning analytics, cloud-native infrastructure, and automated cybersecurity mechanisms to enhance threat detection, infrastructure automation, and continuous compliance management. The architecture incorporates intelligent monitoring, predictive vulnerability analysis, and automated response capabilities to ensure resilient and secure digital ecosystems. The proposed model demonstrates how AI-driven cloud systems can improve operational efficiency, protect sensitive data, and enable reliable digital services in healthcare, financial systems, and smart infrastructure environments.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Smart Infrastructure
  topic: keywords
subject: Healthcare Systems
  topic: keywords
subject: Financial Systems
  topic: keywords
subject: Cloud-Native Architecture
  topic: keywords
creator: Ankur Chaudhary
rights: Copyright (c) 2026
date: 2026-02-15
publication date: 2026-02-15
starting page: 13624
ending page: 13629
doi: 10.15662/IJRAI.2026.0901015

Secure HR Data Exchange between SAP SuccessFactors and Payroll Using AI-Optimized Encryption, Masking, and Data Minimization Controls
type: item
description: Secure exchange of human resource data between cloud based human capital management platforms and downstream payroll systems has become a critical enterprise concern as organizations increasingly rely on distributed integration architectures. This study examines the confidentiality, integrity, and exposure risks inherent in data flows between SAP SuccessFactors and payroll platforms, with particular attention to personally identifiable and financial information subject to stringent regulatory obligations. The paper argues that traditional perimeter based security and static encryption alone are insufficient to address overexposure, misuse, and operational leakage of sensitive HR attributes during routine payroll processing. To address this gap, the study proposes a unified control framework that combines strong cryptographic protection, policy driven masking, and strict data minimization, augmented by AI optimized decision logic appropriate to the technological landscape of 2019. The framework leverages statistical risk scoring, rule augmented learning, and anomaly detection techniques to dynamically select encryption strength, masking profiles, and attribute level payload composition based on data sensitivity, purpose limitation, and operational context. A reference architecture is presented to demonstrate how these controls can be embedded across extraction, transformation, transmission, and ingestion layers without disrupting payroll accuracy or timeliness. Evaluation considerations focus on measurable reductions in data exposure, improved audit evidence generation, and enhanced governance transparency rather than speculative automation claims. The findings suggest that AI optimized security orchestration can materially improve trust and compliance in HR data exchanges while remaining compatible with established enterprise integration patterns. This work contributes a practical and extensible foundation for secure HR system interoperability and offers a defensibl…
subject: Articles
  topic: section
subject: SAP SuccessFactors integration
  topic: keywords
subject: payroll data security
  topic: keywords
subject: HR data exchange
  topic: keywords
subject: enterprise encryption architecture
  topic: keywords
subject: data masking controls
  topic: keywords
subject: data minimization strategy
  topic: keywords
subject: AI assisted security optimization
  topic: keywords
subject: policy driven access control
  topic: keywords
subject: GDPR compliant HR systems
  topic: keywords
subject: cryptographic key management
  topic: keywords
subject: sensitive data governance
  topic: keywords
subject: enterprise integration risk management
  topic: keywords
creator: Manoj Parasa
rights: Copyright (c) 2026
date: 2026-01-14
publication date: 2026-01-14
starting page: 13609
ending page: 13623
doi: 10.15662/IJRAI.2026.0901014

From Reactive Alerts to Predictive Intelligence: AI-Assisted Monitoring in Modern Cloud Environments
type: item
description: Modern distributed systems operating across cloud-native, microservices-based, and containerized environments generate massive volumes of high-velocity telemetry data, including logs, metrics, traces, configuration changes, and event streams. As system architectures grow increasingly decentralized and elastic, traditional rule-based monitoring approaches dependent on static thresholds and manually crafted alerts struggle to cope with dynamic workloads, ephemeral infrastructure, and evolving failure modes. These limitations often result in excessive false positives, alert fatigue, delayed root cause identification, and reactive incident management practices. Artificial Intelligence (AI) and Machine Learning (ML) techniques have therefore emerged as critical enablers of proactive incident detection, leveraging unsupervised, semi-supervised, and statistical learning models to automatically identify anomalous behavior patterns before they escalate into service degradation or outages. By incorporating scalable anomaly detection algorithms such as Isolation Forest, structured log parsing mechanisms like Drain, and real-time statistical learning frameworks for continuous anomaly scoring, AI-assisted monitoring systems transform raw telemetry into actionable insights. This paper presents a comprehensive overview of these AI-driven monitoring architectures, examining their algorithmic foundations, data preprocessing pipelines, streaming inference capabilities, and operational trade-offs, while highlighting how they collectively support early detection, adaptive learning, and resilience in modern production environments.
subject: Articles
  topic: section
subject: AI-assisted monitoring
  topic: keywords
subject: proactive incident detection
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: AIOps
  topic: keywords
subject: log analytics
  topic: keywords
subject: Isolation Forest
  topic: keywords
subject: Drain
  topic: keywords
subject: statistical learning
  topic: keywords
subject: predictive monitoring
  topic: keywords
subject: observability
  topic: keywords
creator: Shekar Vollem
rights: Copyright (c) 2023
date: 2023-01-09
publication date: 2023-01-09
starting page: 8337
ending page: 8345
doi: 10.15662/IJRAI.2023.0601009

Machine Learning-Enabled Predictive Security and Governance Frameworks for SAP-Integrated Cloud-Native Enterprise Systems
type: item
description: The rapid transformation of enterprise digital ecosystems has led organizations to increasingly adopt cloud-native architectures integrated with enterprise resource planning platforms such as SAP. While these advancements provide greater scalability, operational efficiency, and real-time analytics capabilities, they also introduce complex security and governance challenges. Enterprise environments today must manage large volumes of sensitive data, distributed workloads, and multiple access points across hybrid and multi-cloud infrastructures. As a result, organizations require intelligent and predictive security mechanisms capable of proactively detecting threats, ensuring regulatory compliance, and maintaining governance standards across enterprise systems. Machine learning technologies have emerged as powerful tools for analyzing large-scale operational data and identifying patterns associated with potential cyber threats or governance violations. This research proposes a machine learning-enabled predictive security and governance framework designed specifically for SAP-integrated cloud-native enterprise environments. The framework combines AI-driven anomaly detection models, identity-centric governance policies, and automated monitoring mechanisms to improve enterprise security posture and operational reliability. The study evaluates the proposed framework through simulated enterprise infrastructure environments that generate large-scale operational logs, user activity records, and system transaction data. Experimental results demonstrate that predictive machine learning models significantly enhance threat detection accuracy and reduce response times compared to traditional rule-based security systems. Furthermore, the framework improves enterprise governance by enabling continuous monitoring of access control policies and compliance requirements. The research findings highlight the potential of integrating machine learning with cloud-native enterprise architectu…
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Predictive Security
  topic: keywords
subject: SAP Enterprise Systems
  topic: keywords
subject: Cloud-Native Architecture
  topic: keywords
subject: Cybersecurity Analytics
  topic: keywords
subject: Identity Governance
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Enterprise Risk Management
  topic: keywords
subject: Intelligent Monitoring Systems
  topic: keywords
subject: Digital Enterprise Security
  topic: keywords
creator: Ajay Chakravarty
rights: Copyright (c) 2023
date: 2023-09-25
publication date: 2023-09-25
starting page: 9544
ending page: 9551
doi: 10.15662/IJRAI.2023.0605011

Intelligent Data Lakehouse and MLOps Pipelines for Scalable Predictive Analytics in Cloud-Based Enterprise Systems
type: item
description: Enterprise systems increasingly rely on large-scale, cloud-based data platforms to support decision-making, operational efficiency, and strategic planning. Traditional data warehouses and lakes often struggle with scalability, integration, and real-time analytics requirements. The emergence of data lakehouse architectures, combined with machine learning operations (MLOps) pipelines, provides an integrated solution for scalable, high-performance predictive analytics in modern enterprise environments. This research proposes an intelligent data lakehouse architecture integrated with MLOps pipelines to enable scalable predictive analytics in cloud-based enterprise systems. The framework unifies structured, semi-structured, and unstructured data while supporting real-time ingestion, preprocessing, feature engineering, and model deployment. MLOps pipelines automate model training, testing, versioning, deployment, and monitoring, ensuring reproducibility, reliability, and continuous improvement of predictive models. The architecture leverages cloud-native technologies, including distributed storage, containerized services, and orchestration tools, to optimize resource allocation and scalability. Predictive analytics models provide insights for operational optimization, financial forecasting, customer behavior analysis, and risk assessment. The study highlights the benefits of combining intelligent data lakehouses with MLOps pipelines, including improved model performance, operational efficiency, and governance, while addressing challenges such as data heterogeneity, pipeline complexity, and cross-cloud interoperability.
subject: Articles
  topic: section
subject: Intelligent Data Lakehouse
  topic: keywords
subject: MLOps Pipelines
  topic: keywords
subject: Cloud-Based Enterprise Systems
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Scalable Analytics
  topic: keywords
subject: Data Integration
  topic: keywords
subject: Feature Engineering
  topic: keywords
subject: Model Deployment
  topic: keywords
subject: Cloud-Native Architecture
  topic: keywords
creator: Samuel Kaski
rights: Copyright (c) 2025
date: 2025-12-29
publication date: 2025-12-29
starting page: 13135
ending page: 13142
doi: 10.15662/IJRAI.2025.0806036

Machine Learning–Enabled Enterprise Lakehouse and Multi-Cloud Data Architecture for Scalable Financial and Business Intelligence Systems
type: item
description: In the era of digital transformation, organizations generate massive volumes of structured and unstructured data from financial systems, enterprise applications, customer interactions, and external platforms. Traditional data warehouse architectures often struggle to handle the increasing complexity, scalability requirements, and real-time analytics demands of modern enterprises. The emergence of data lakehouse architectures and multi-cloud infrastructures has created new opportunities for building scalable and intelligent data ecosystems. This research explores a machine learning–enabled enterprise lakehouse architecture integrated with multi-cloud environments to support scalable financial analytics and business intelligence systems. The proposed architecture combines the flexibility of data lakes with the structured performance capabilities of data warehouses, enabling efficient storage, processing, and analytics of large-scale enterprise data. Machine learning models are integrated within the architecture to automate data analysis, detect patterns, predict financial trends, and support strategic decision-making. The multi-cloud approach ensures high availability, scalability, and vendor flexibility by distributing workloads across multiple cloud platforms. Additionally, the architecture emphasizes secure data governance, data quality management, and real-time analytics capabilities required for enterprise financial operations. The study highlights the benefits of integrating machine learning with lakehouse platforms for advanced analytics while addressing challenges related to data integration, infrastructure complexity, and operational costs.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Enterprise Lakehouse
  topic: keywords
subject: Multi-Cloud Architecture
  topic: keywords
subject: Financial Analytics
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Big Data
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Scalable Data Systems
  topic: keywords
subject: Cloud Computing
  topic: keywords
creator: Andreas Geiger
rights: Copyright (c) 2025
date: 2025-07-17
publication date: 2025-07-17
starting page: 12688
ending page: 12698
doi: 10.15662/IJRAI.2025.0804017

AI-Augmented Cloud Monitoring Platforms for Predictive Infrastructure Reliability
type: item
description: Given the growing reliance of individuals and organizations on cloud computing services, prediction of failures and assurance of the reliability of these services have become paramount to minimizing associated costs. The observability provided by the telemetry of such services offers data-driven signals for the prediction of failures, as well as indicators of timing and policies for optimal, efficient, cost-effective operation. Experimental services have demonstrated the viability of applying state-of-the-art artificial intelligence techniques—anomaly detection, forecasting, root cause analysis, and recommendation systems—to the telemetry data of cloud service providers in novel ways. These services are ready for deployment, helping enterprises monitor their services, detect anomalies, and predict their future activity, among other needs. Nevertheless, success depends on a careful balance between the quality and the quantity of the telemetry data consumed. Despite significant resources devoted to the telemetry of cloud services, privacy-preserving monitoring remains a challenge. Failure prevention relies on tailored indicators of each service’s failure modes and time-to-failure windows, as well as a hypothetical cost-benefit analysis. Even with sufficient percentages, the predictive models of these signals require careful definition, evaluation, and monitoring. Dynamic sizing of resources and autoscaling policies can help minimize costs while guaranteeing quality of service, particularly when intertwined with the resource allocation policies of the cloud provider. A service can improve its overall stability and performance by controlling its own scaling policies.
subject: Articles
  topic: section
subject: Observability
  topic: keywords
subject: telemetry
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: forecasting
  topic: keywords
subject: monitoring-as-a-service
  topic: keywords
subject: predictive maintenance
  topic: keywords
subject: dynamic autoscaling
  topic: keywords
subject: AI
  topic: keywords
subject: cloud reliability
  topic: keywords
subject: cloud operations
  topic: keywords
subject: root-cause analysis
  topic: keywords
subject: causal inference
  topic: keywords
subject: SRE
  topic: keywords
subject: observability maturity
  topic: keywords
subject: software reliability engineering
  topic: keywords
subject: SLAs
  topic: keywords
subject: MTTR
  topic: keywords
subject: prediction horizon
  topic: keywords
subject: time-to-repair
  topic: keywords
subject: data quality
  topic: keywords
subject: data-completeness
  topic: keywords
subject: data-privacy
  topic: keywords
subject: monitoring drift
  topic: keywords
subject: adversarial machine-learning
  topic: keywords
creator: Dileep Valiki
rights: Copyright (c) 2022
date: 2022-12-27
publication date: 2022-12-27
starting page: 8162
ending page: 8179
doi: 10.15662/IJRAI.2022.0506027

Multi-Agent AI Architectures for Automated Customer Experience Management Platforms
type: item
description: A multi-agent approach supports more complex, realistic, and timely interaction scenarios for both customers and companies. Instead of developing monolithic dialogue systems that can support requests from any customer, multi-agent systems permit dedicated agents for specific tasks, offering richer content and handling more intricate interactions. Dialogue systems based on either hierarchical or federated multi-agent architectures are reviewed, and these two patterns are explored as foundations for customer experience management platforms in which multiple agents can automate a variety of customer interaction scenarios. A multi-agent approach supports more complex, realistic, and timely interaction scenarios for both customers and companies. Instead of developing monolithic dialogue systems that can support requests from any customer, multi-agent systems permit dedicated agents for specific tasks, offering richer content and handling more intricate interactions. Dialogue systems based on either hierarchical or federated multi-agent architectures are reviewed, and these two patterns are explored as foundations for customer experience management platforms in which multiple agents can automate a variety of customer interaction scenarios.
subject: Articles
  topic: section
subject: Multi-Agent Systems (MAS)
  topic: keywords
subject: Customer Experience Management Platforms
  topic: keywords
subject: Automated Dialogue Systems
  topic: keywords
subject: Hierarchical Multi-Agent Architectures
  topic: keywords
subject: Federated Multi-Agent Architectures
  topic: keywords
subject: Conversational AI Agents
  topic: keywords
subject: Task-Specific Intelligent Agents
  topic: keywords
subject: Distributed Dialogue Management
  topic: keywords
subject: Customer Interaction Automation
  topic: keywords
subject: Human–Agent Communication Models
  topic: keywords
subject: Context-Aware Conversation Design
  topic: keywords
subject: Intent Recognition and Routing
  topic: keywords
subject: Modular AI System Design
  topic: keywords
subject: Scalable Service-Oriented Architectures
  topic: keywords
subject: Intelligent Customer Support Systems
  topic: keywords
subject: Agent Coordination Mechanisms
  topic: keywords
subject: Adaptive Interaction Strategies
  topic: keywords
subject: Enterprise Conversational Platforms
  topic: keywords
subject: AI-Driven Customer Engagement
  topic: keywords
subject: Complex Multi-Turn Interaction Handling
  topic: keywords
creator: Vinod Battapothu
rights: Copyright (c) 2024
date: 2024-12-29
publication date: 2024-12-29
starting page: 11929
ending page: 11946
doi: 10.15662/IJRAI.2024.0706038

AI-Driven Incident Prediction Models for Proactive IT Operations Management
type: item
description: The combination of AI and Big Data has opened many valuable opportunities for the digital enterprise across various business segments. One area that lacks sufficient attention, especially from the AI community, is proactive (event) management of enterprises’ IT operations. Proactive IT operation management constitutes one of the future trends of IT services, with the promise of lowering the cost of service outages and failures. Most organizations still perform IT operations in a passive manner, implementing preventive measures and performing incident and problem management instead of predicting service outages in advance. As a result, despite their demand and potential, such tools are usually not part of the operational toolbox of most organizations. Building on the available data streams, novel ML models and tools can be designed to predict failures in enterprise IT environments, service outages and IT service degradation. The information produced can be fully exploited in the context of enterprise monitoring, capacity management and business continuity management, and the generated alerts can assist incident management teams. These solutions assist organizations in moving toward a more proactive IT operations management strategy. The models can also serve other areas apart from predictive incident management. In any use case where a time series has to be closely monitored and where sudden changes can have a large impact, the proposed approach can be used. Use cases include real-time alerting and thresholding, predictive capacity planning and budget forecasting.
subject: Articles
  topic: section
subject: Service operation management
  topic: keywords
subject: IT Systems Management
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: time series forecasting
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: SRE
  topic: keywords
subject: ITSM
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: machine learning
  topic: keywords
subject: supervised learning
  topic: keywords
subject: unsupervised learning
  topic: keywords
subject: data science
  topic: keywords
subject: data-driven decision making
  topic: keywords
subject: correlation analysis
  topic: keywords
subject: classification
  topic: keywords
subject: capacity management
  topic: keywords
subject: performance management
  topic: keywords
subject: monitoring
  topic: keywords
subject: resource provisioning
  topic: keywords
subject: budgets
  topic: keywords
subject: resource usage
  topic: keywords
subject: incident management
  topic: keywords
subject: IT incidents
  topic: keywords
creator: Nareddy Abhireddy
rights: Copyright (c) 2023
date: 2023-12-28
publication date: 2023-12-28
starting page: 10015
ending page: 10030
doi: 10.15662/IJRAI.2023.0606028

Next Generation Intelligent Enterprise Framework Integrating Generative AI IoT Analytics Predictive Security and Scalable Hybrid Cloud Infrastructure
type: item
description: The rapid convergence of Artificial Intelligence, Internet of Things (IoT), and hybrid cloud computing is redefining enterprise digital transformation. Modern enterprises require intelligent, scalable, and secure frameworks capable of processing massive real-time data streams while ensuring predictive threat mitigation and operational efficiency. This paper proposes a Next Generation Intelligent Enterprise Framework integrating Generative AI, IoT Analytics, Predictive Security, and Scalable Hybrid Cloud Infrastructure. The framework leverages Generative AI models for automated decision support, content synthesis, anomaly explanation, and adaptive business workflows. IoT analytics enables real-time data acquisition, edge processing, and predictive maintenance across distributed environments. Predictive security mechanisms employ machine learning for proactive threat detection, risk scoring, and automated response orchestration. Hybrid cloud infrastructure ensures elastic scalability, workload portability, and regulatory compliance across on-premise and public cloud environments. The proposed architecture introduces multi-layered intelligence, secure data pipelines, containerized microservices, and automated DevSecOps governance. Experimental modeling demonstrates improved operational resilience, reduced downtime, enhanced predictive accuracy, and optimized resource utilization. This research contributes a comprehensive architectural model and implementation methodology for enterprises transitioning toward AI-driven autonomous ecosystems.
subject: Articles
  topic: section
subject: Generative AI
  topic: keywords
subject: IoT Analytics
  topic: keywords
subject: Predictive Security
  topic: keywords
subject: Hybrid Cloud Architecture
  topic: keywords
subject: Enterprise AI Framework
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Autonomous Systems
  topic: keywords
subject: DevSecOps Automation
  topic: keywords
subject: Microservices Architecture
  topic: keywords
subject: AI Governance
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Smart Enterprise Systems
  topic: keywords
creator: Alberto Bifet
rights: Copyright (c) 2026
date: 2026-02-18
publication date: 2026-02-18
starting page: 13600
ending page: 13608
doi: 10.15662/IJRAI.2026.0901013

Measuring Business Value of End-to-End Quote-to-Cash with SAP CPQ + CRM + ECC/S4HANA
type: item
description: This paper examines measurement frameworks for assessing business value of integrated quote-to-cash (Q2C) processes spanning SAP CPQ, SAP CRM, and SAP ECC/S4HANA systems. Through secondary research, this study explores key performance indicators, value realization mechanisms, and measurement methodologies for end-to-end Q2C implementations. Evidence demonstrates that integrated Q2C implementations reduce cycle times by 35-50%, improve forecast accuracy by 25-40%, and increase revenue capture by 5-15% through reduced leakage. Organizations implementing comprehensive Q2C strategies achieve competitive advantages through operational efficiency, improved cash flow, and enhanced customer experience. This paper synthesizes findings from academic literature, industry research, and case studies to provide frameworks for measuring and realizing Q2C business value.
subject: Articles
  topic: section
subject: quote-to-cash
  topic: keywords
subject: SAP CPQ
  topic: keywords
subject: SAP CRM
  topic: keywords
subject: SAP S4HANA
  topic: keywords
subject: business value measurement
  topic: keywords
subject: process integration
  topic: keywords
subject: revenue optimization
  topic: keywords
creator: Vinayak Kalabhavi
rights: Copyright (c) 2026
date: 2026-03-05
publication date: 2026-03-05
starting page: 12
ending page: 20
doi: 10.15662/IJRAI.2026.0902002

Zero Trust AI Architecture for Enterprise Healthcare Risk Governance in Cloud Ecosystems with Secure Data Encryption
type: item
description: The accelerated adoption of cloud computing and artificial intelligence (AI) in healthcare has significantly enhanced predictive risk analytics, operational efficiency, and patient-centered care. However, increased data exchange, distributed infrastructure, and AI-driven automation expose healthcare enterprises to heightened cybersecurity, privacy, and governance risks. Traditional perimeter-based security models are insufficient in protecting sensitive health data within complex cloud ecosystems. This research proposes a Zero Trust AI Architecture designed specifically for Enterprise Healthcare Risk Governance, integrating secure data encryption, identity-centric access control, continuous verification, and AI governance mechanisms. The proposed framework adopts a “never trust, always verify” security paradigm across cloud-native AI pipelines. It embeds encryption at rest and in transit, role-based and attribute-based access controls, multi-factor authentication, secure API gateways, and continuous monitoring. AI risk governance modules incorporate model validation, bias detection, audit trails, explainability tools, and regulatory compliance alignment with HIPAA and GDPR standards. By combining Zero Trust principles with adaptive AI governance in cloud environments, healthcare enterprises can mitigate cyber threats, prevent unauthorized data access, ensure regulatory compliance, and maintain operational resilience. This study provides a comprehensive enterprise architecture and implementation methodology for secure, trustworthy, and compliant healthcare AI risk ecosystems.
subject: Articles
  topic: section
subject: Zero Trust Architecture
  topic: keywords
subject: Healthcare AI Governance
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: Secure Data Encryption
  topic: keywords
subject: Enterprise Risk Management, HIPAA Compliance
  topic: keywords
subject: AI Risk Governance
  topic: keywords
subject: Identity Access Management
  topic: keywords
subject: Cybersecurity in Healthcare
  topic: keywords
subject: Secure Cloud AI
  topic: keywords
creator: Sandeep Gupta
rights: Copyright (c) 2025
date: 2025-11-26
publication date: 2025-11-26
starting page: 13126
ending page: 13134
doi: 10.15662/IJRAI.2025.0806035

Architecting Near Real Time Data Integration Pipelines with PowerExchange and IICS Streaming
type: item
description: By February 2019, enterprises operating large scale transactional platforms increasingly required data integration architectures capable of delivering near real time data movement with strong reliability and minimal impact on source systems. Traditional batch oriented extract transform load processes, while still widely used, were insufficient for use cases involving operational analytics, customer facing applications, regulatory reporting, and downstream system synchronization. Organizations sought integration solutions that could propagate data changes continuously while maintaining transactional integrity and operational stability. Change Data Capture had become a well established technique for addressing these requirements by enabling incremental data movement based on committed changes in source systems. Informatica PowerExchange was widely adopted in enterprise environments as a log based CDC solution capable of capturing database changes with low latency and minimal overhead. At the same time, Informatica Intelligent Cloud Services introduced streaming ingestion and processing capabilities that allowed CDC events to be transported and consumed in near real time within cloud based integration pipelines. This paper examines near real time data integration architectures that combine PowerExchange for change capture with IICS Streaming for event transport and processing, as understood and implemented by early 2019. The discussion focuses on architectural patterns for integrating on premises transactional systems with cloud based targets, emphasizing reliability, ordering, scalability, and operational governance. Particular attention is given to how CDC streams were modeled, buffered, and consumed to support continuous data propagation without tightly coupling source and target systems. Finally, the paper synthesizes design considerations and operational trade offs associated with PowerExchange and IICS Streaming based integration pipelines. These considerations i…
subject: Articles
  topic: section
subject: Near real time data integration
  topic: keywords
subject: change data capture
  topic: keywords
subject: Informatica PowerExchange
  topic: keywords
subject: IICS Streaming
  topic: keywords
subject: log based replication
  topic: keywords
subject: enterprise data integration
  topic: keywords
subject: cloud data pipelines
  topic: keywords
subject: asynchronous data movement
  topic: keywords
subject: transactional consistency
  topic: keywords
subject: streaming ingestion
  topic: keywords
subject: These keywords capture the technical and architectural focus of enterprise data integration practices as of February 2019
  topic: keywords
subject: emphasizing continuous data movement
  topic: keywords
subject: hybrid on premises and cloud architectures
  topic: keywords
subject: reliable propagation of transactional changes across distributed systems
  topic: keywords
creator: Srujana Parepalli
rights: Copyright (c) 2019
date: 2019-02-05
publication date: 2019-02-05
starting page: 933
ending page: 943
doi: 10.15662/IJRAI.2019.0201004

An Apache Spark Driven AI Framework for Enterprise Healthcare Analytics with Genetic Algorithm Optimization and Blockchain Security
type: item
description: Enterprise healthcare organizations generate vast volumes of heterogeneous data from electronic health records, medical imaging systems, laboratory databases, wearable devices, and insurance platforms. Efficiently processing and securing this data while extracting actionable intelligence remains a critical challenge. This study proposes an Apache Spark–driven Artificial Intelligence framework that integrates Machine Learning (ML), Genetic Algorithm (GA) optimization, and Blockchain-based security for enterprise healthcare analytics. The framework leverages Apache Spark for distributed in-memory processing and scalable machine learning, supported by Apache Hadoop for reliable data storage. Genetic Algorithms are applied for feature selection, hyperparameter tuning, and dynamic resource optimization to enhance predictive performance and computational efficiency. Blockchain technology, implemented through Hyperledger Fabric, ensures secure, transparent, and tamper-proof data exchange among healthcare stakeholders. The integrated architecture improves prediction accuracy, reduces processing latency, enhances data integrity, and optimizes cloud resource utilization. Experimental evaluation demonstrates significant performance gains compared to conventional healthcare analytics systems. The proposed framework provides a scalable, secure, and intelligent solution for enterprise healthcare decision-making in distributed cloud environments.
subject: Articles
  topic: section
subject: Enterprise Healthcare Analytics
  topic: keywords
subject: Apache Spark
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Genetic Algorithm Optimization
  topic: keywords
subject: Blockchain Security
  topic: keywords
subject: Hyperledger Fabric
  topic: keywords
subject: Distributed Cloud Computing
  topic: keywords
subject: Big Data Analytics
  topic: keywords
subject: Secure Healthcare Systems
  topic: keywords
creator: Meenu Dave
rights: Copyright (c) 2024
date: 2024-11-20
publication date: 2024-11-20
starting page: 11920
ending page: 11928
doi: 10.15662/IJRAI.2024.0706037

Cloud-Native AI Pipelines for Real-Time Cyber Threat Intelligence
type: item
description: Cloud-native pipelines are expected to be able to analyze data in cloud data lakes using a reconciliation process to detect and process new types of tactical events relating to cyber threat intelligence. Data-related, source-related, and service-related considerations and requirements for real-time pipelines that deploy deep learning processes to create Cloud-native AI Pipeline for Ransomware Detection are discussed. The architecture is open-source-based, with a collection of repositories containing several online learning processes using cloud-native AI techniques. Cyber threat detection is a dynamic process that depends on the existence of detections of malicious event patterns from indicators and triggers for reactive responses by using relevant updated data sources. The technical challenge derives from the need for these detections, supporting indicators, and tactical action responses to be created and activated in a real-time automated fashion. An extensible and scalable event correlation engine has been proposed, capable of detecting malicious events occurring in real time. However, detection of new types of events is hampered by the requirement for expert-driven feature-extraction processes and supervised learning classifiers capable of recognizing only specific threats, limiting the detection space to only those attack types for which the model has been previously trained. In online learning processes under continuous deployment, the model is continuously trained as new data is ingested in the base, thus enabling the recognition of new types of events as they appear. Distributed cloud-native process architecture enables the continual deployment of adaptation processes into production that can use data stored in cloud data lakes for continuous retraining and adaptation. A new, integrated, end-to-end visual Cloud-native AI Pipeline for Ransomware Detection based on the recognition of the modus operandi of ransomware is implemented. Data-related, source-related…
subject: Articles
  topic: section
subject: Cloud-Native AI Pipelines
  topic: keywords
subject: Ransomware Detection Systems
  topic: keywords
subject: Cyber Threat Intelligence (CTI)
  topic: keywords
subject: Real-Time Event Correlation Engines
  topic: keywords
subject: Deep Learning for Cybersecurity
  topic: keywords
subject: Online Learning Architectures
  topic: keywords
subject: Continuous Model Retraining
  topic: keywords
subject: Cloud Data Lake Analytics
  topic: keywords
subject: Tactical Event Reconciliation
  topic: keywords
subject: Distributed Cloud-Native Processing
  topic: keywords
subject: Malicious Pattern Recognition
  topic: keywords
subject: Adaptive Threat Detection Models
  topic: keywords
subject: Open-Source Security Frameworks
  topic: keywords
subject: Automated Incident Response Systems
  topic: keywords
subject: Indicator and Trigger-Based Detection
  topic: keywords
subject: Scalable Microservices Architectures
  topic: keywords
subject: Continuous Deployment (CD) in AI Systems
  topic: keywords
subject: Modus Operandi-Based Detection
  topic: keywords
subject: Feature Engineering in Cybersecurity
  topic: keywords
subject: Intelligent Security Operations Pipelines
  topic: keywords
creator: Dileep Valiki
rights: Copyright (c) 2024
date: 2024-12-29
publication date: 2024-12-29
starting page: 11904
ending page: 11919
doi: 10.15662/IJRAI.2024.0706036

AI-Driven Autonomous Knowledge Assistants for Enterprise IT Helpdesks
type: item
description: In today’s complex information technology (IT) environment, enterprises utilise increasingly sophisticated products and know-how to support critical business operations. Conventional IT assistance mechanisms are costly in terms of time, human resources, and finances. An alternative strategy is to implement an enterprise IT helpdesk knowledge assistant to answer questions and solve problems, primarily for first- and second-line IT Support and Service Desks. Such an autonomous knowledge assistant is an augmented and continuously assisted chatbot powered by artificial intelligence (AI) technologies. It supports omnichannel access, replicates the judgement and experience of human experts, continuously gathers insights from support conversations, provides recommendations for good practices and detection-prevention-action workflows, and ensures data governance. Although the enterprise knowledge assistant is primarily geared towards IT, it can be adapted for self-service in different business functions and industries. AI-driven autonomous knowledge assistants deploy AI technologies, including advanced Natural Language Processing (NLP) techniques, to ingest content, train large Transformer text generators with dedicated knowledge, and converse with users, drawing on internal company resources such as documentation, previously closed incidents, troubleshooting guides, run-book procedures, best practices, and ServiceNow CMDB data. Users may be service desk agents, system administrators, remote work users, colleagues providing assistance, or anyone else engaged in the operations and maintenance of the IT enterprise systems. These autonomous knowledge assistants overcome the data-privacy challenges of ChatGPT and similar systems by ingesting external content to create their own proprietary large language model.
subject: Articles
  topic: section
subject: Enterprise IT helpdesk
  topic: keywords
subject: autonomous self-service assistant
  topic: keywords
subject: autonomous knowledge assistant
  topic: keywords
subject: knowledge management
  topic: keywords
subject: data governance
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: data privacy
  topic: keywords
subject: AI bias
  topic: keywords
subject: knowledge graph
  topic: keywords
subject: operational automation
  topic: keywords
subject: intelligent IT assistant
  topic: keywords
subject: virtual assistant
  topic: keywords
subject: user experience
  topic: keywords
subject: autonomous troubleshooting
  topic: keywords
subject: active knowledge base
  topic: keywords
creator: Anumandla Mukesh
rights: Copyright (c) 2023
date: 2023-12-28
publication date: 2023-12-28
starting page: 10000
ending page: 10014
doi: 10.15662/IJRAI.2023.0606027

Cognitive AI for Autonomous Security Operations Hybrid Threat Detection Intrusion Avoidance and SOC Resilience in Cloud-Native Ecosystems
type: item
description: The rapid evolution of cloud-native ecosystems has transformed enterprise infrastructure, introducing unprecedented scalability, agility, and complexity. However, this transformation has also expanded the threat landscape, exposing organizations to hybrid cyber threats that combine traditional attack vectors with advanced persistent techniques. Security Operations Centers (SOCs) face mounting challenges in managing alert fatigue, skill shortages, and increasingly sophisticated adversaries. Cognitive Artificial Intelligence (AI) offers a transformative approach to autonomous security operations by integrating machine learning, deep learning, natural language processing, and behavioral analytics into unified defense architectures. This paper explores how cognitive AI enhances hybrid threat detection, intrusion avoidance, and SOC resilience within cloud-native environments such as containers, microservices, and Kubernetes orchestration frameworks. By leveraging predictive analytics, automated incident response, and adaptive learning mechanisms, cognitive AI enables real-time anomaly detection and proactive mitigation of threats. The study examines architectural frameworks, operational models, and implementation methodologies that enable secure, scalable, and self-healing security ecosystems. It further evaluates advantages, limitations, and ethical considerations associated with AI-driven security automation. The findings highlight that cognitive AI significantly improves detection accuracy, response speed, and operational efficiency while redefining the future of autonomous cybersecurity governance.
subject: Articles
  topic: section
subject: Cognitive AI
  topic: keywords
subject: Autonomous Security Operations
  topic: keywords
subject: Hybrid Threat Detection
  topic: keywords
subject: Intrusion Avoidance
  topic: keywords
subject: SOC Resilience
  topic: keywords
subject: Cloud-Native Security
  topic: keywords
subject: Zero Trust
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Behavioral Analytics
  topic: keywords
subject: Kubernetes Security
  topic: keywords
subject: Security Automation
  topic: keywords
subject: AIOps
  topic: keywords
subject: DevSecOps
  topic: keywords
creator: Alexandru Costan
rights: Copyright (c) 2024
date: 2024-07-17
publication date: 2024-07-17
starting page: 11117
ending page: 11126
doi: 10.15662/IJRAI.2024.0704013

Scalable Cloud and AI Integration for Intelligent Transportation Safety Systems
type: item
description: Cyber-Physical Systems (CPSs) integrate connected sensing (e.g., LiDAR, cameras), control, computing, wireless communication, and actuation components with physical objects and processes to achieve interactive and adaptive capacity. CPSs can improve safety for autonomous systems, such as Autonomous Land Vehicles (ALVs) and Uncrewed Aerial Vehicles (UAVs), by identifying and avoiding hazardous events during operation. However, the recent surge of application scenarios, traffic data requirements, and the number and size of interactive CPS participants have led to significant challenges in improving and ensuring operational safety for these intelligent CPSs. These challenges have prompted scholars and practitioners to integrate cloud infrastructure and Artificial Intelligence (AI) services with CPSs to create new cloud-enabled Cyber-Physical Systems (C-CPSs). These C-CPSs can reduce the validation and training cost of intelligence models and intelligently respond to unsafe events for traffic safety enhancement. In the context of Intelligent Transportation Systems, the combination of C-CPSs, Drones as a Service (DaaS), and ALV safety is addressed.
subject: Articles
  topic: section
subject: Cyber-Physical Systems (CPS)
  topic: keywords
subject: Cloud-Enabled CPS (C-CPS)
  topic: keywords
subject: Intelligent Transportation Systems
  topic: keywords
subject: Autonomous Land Vehicles (ALVs)
  topic: keywords
subject: Uncrewed Aerial Vehicles (UAVs)
  topic: keywords
subject: Drones as a Service (DaaS)
  topic: keywords
subject: AI-Integrated Safety Systems
  topic: keywords
subject: LiDAR and Vision Sensing Fusion
  topic: keywords
subject: Cloud-Based Model Training
  topic: keywords
subject: Traffic Safety Enhancement
  topic: keywords
subject: Edge-to-Cloud CPS Architectures
  topic: keywords
subject: Hazard Event Detection
  topic: keywords
subject: Real-Time Autonomous Control
  topic: keywords
subject: Scalable CPS Infrastructure
  topic: keywords
subject: AI-Driven Traffic Analytics
  topic: keywords
subject: Connected Vehicle Ecosystems
  topic: keywords
subject: Adaptive Safety Mechanisms
  topic: keywords
subject: Distributed Sensing and Actuation
  topic: keywords
subject: Operational Risk Mitigation in CPS
  topic: keywords
subject: Intelligent Mobility Platforms
  topic: keywords
creator: Vinod Battapothu
rights: Copyright (c) 2024
date: 2024-12-25
publication date: 2024-12-25
starting page: 11888
ending page: 11903
doi: 10.15662/IJRAI.2024.0706035

Leveraging Cloud-Enabled AI for Intelligent Traffic Prediction and Sustainable Urban Systems
type: item
description: Urban mobility is a major concern for large metropolitan areas. Cloud-enabled Artificial Intelligence (AI) technology may help to manage travel demand and offer a more sustainable urban mobility model for smart cities. An AI-driven, cloud-enabled architectural framework applied to traffic management and control business processes is proposed. It integrates four AI-based predictive models with real-time traffic control and incident-response systems. Different data-driven use cases, proved in two metropolitan areas, illustrate the applicability of this framework in real operational environments. Results show that this approach is capable of managing urban traffic in real time. In addition, it demonstrates how AI-based models can be developed, deployed, and operated within cloud environments, offering a decision-support capability. The central role of predictive traffic management systems in the cloud-enabled AI model for smart cities is assessed. Demand management processes and the integration of Mobility-as-a-Service platforms are also analyzed. These are mandatory steps to offer a sustainable traffic model for large metropolitan areas. Finally, other aspects such as environmental protection and energy consumption in urban mobility are examined. Traffic management by predictive systems enables a more accurate confrontation of real traffic conditions, improving the energy, environmental, and resilience contexts.
subject: Articles
  topic: section
subject: Smart City Traffic Management
  topic: keywords
subject: Cloud-Enabled Artificial Intelligence
  topic: keywords
subject: AI-Driven Urban Mobility
  topic: keywords
subject: Predictive Traffic Management Systems
  topic: keywords
subject: Real-Time Traffic Control
  topic: keywords
subject: Intelligent Incident Response
  topic: keywords
subject: Urban Demand Management
  topic: keywords
subject: Mobility-as-a-Service (MaaS)
  topic: keywords
subject: Cloud-Based Decision Support Systems
  topic: keywords
subject: Data-Driven Traffic Optimization
  topic: keywords
subject: Sustainable Urban Mobility Models
  topic: keywords
subject: Metropolitan Traffic Analytics
  topic: keywords
subject: AI Deployment in Cloud Environments
  topic: keywords
subject: Traffic Prediction Models
  topic: keywords
subject: Smart Transportation Architectures
  topic: keywords
subject: Energy-Efficient Urban Mobility
  topic: keywords
subject: Environmental Impact Reduction
  topic: keywords
subject: Resilient Transportation Systems
  topic: keywords
subject: Integrated Traffic Control Frameworks
  topic: keywords
subject: AI-Enabled Smart Cities
  topic: keywords
creator: Mallesham Goli
rights: Copyright (c) 2023
date: 2023-12-20
publication date: 2023-12-20
starting page: 9985
ending page: 9999
doi: 10.15662/IJRAI.2023.0606026

Building the Adaptable Enterprise: Trends in Composable and Event-Driven Salesforce Architectures
type: item
description: This article examines the paradigm shift within the Salesforce ecosystem from monolithic implementations to composable architectures. As organizations face increasing pressure to adapt to rapidly changing business requirements, traditional monolithic systems have revealed significant limitations in flexibility and scalability. The transition toward composable architectures enables businesses to assemble solutions from independent, interchangeable components rather than relying on tightly integrated systems. By leveraging platform capabilities such as Platform Events, MuleSoft APIs, Salesforce Functions, and Lightning Web Components, organizations can create more adaptable enterprise solutions. Event-driven design principles form the foundation of these architectures, facilitating loose coupling between components while supporting real-time responsiveness. The article analyzes architectural patterns, implementation strategies, and performance considerations that organizations should evaluate when adopting composable approaches, highlighting the balance between business agility and technical complexity
subject: Articles
  topic: section
subject: Composable architecture
  topic: keywords
subject: Event-driven design
  topic: keywords
subject: Microservices decomposition
  topic: keywords
subject: Platform events
  topic: keywords
subject: API-led integration
  topic: keywords
creator: Chakra Dhari Gadige
rights: Copyright (c) 2025
date: 2025-12-12
publication date: 2025-12-12
starting page: 13119
ending page: 13125
doi: 10.15662/IJRAI.2025.0806034

Autonomous AI Agents for Enterprise Workflow Orchestration in HR Platforms
type: item
description: Autonomous AI agents are an innovative approach to automating enterprise processes, including on Human Resources (HR) platforms. This work focuses on enterprise workflow orchestration – particularly in the HR domain – by enabling autonomous agents to undertake tasks using existing HR technology stacks. The goal of creating real-world enterprise applications leads to emphasizing the integration of these agents into standard HR platforms and ecosystems. HR platforms manage a variety of workflows across the employee lifecycle, such as recruiting, onboarding, and performance management. Although these workflows often follow a step-by-step pattern with well-defined decisions at each stage, they are currently performed either fully by humans or by utilizing automation tools such as Robotic Process Automation (RPA). These workflows could also be fully automated through the deployment of autonomous agents that would interface with the HR platform and take decisions on behalf of their human counterparts.
subject: Articles
  topic: section
subject: Autonomous AI Agents in HR
  topic: keywords
subject: Enterprise Workflow Orchestration
  topic: keywords
subject: AI-Driven Human Resources Automation
  topic: keywords
subject: Intelligent HR Platform Integration
  topic: keywords
subject: Agent-Based Process Automation
  topic: keywords
subject: AI-Augmented Employee Lifecycle Management
  topic: keywords
subject: Decision-Making Agents in HR Systems
  topic: keywords
subject: Robotic Process Automation (RPA) Evolution
  topic: keywords
subject: Enterprise AI Orchestration Architectures
  topic: keywords
subject: Human-in-the-Loop AI Governance
  topic: keywords
subject: Cognitive Workflow Automation
  topic: keywords
subject: Multi-Agent Enterprise Systems
  topic: keywords
subject: AI-Enabled Recruiting and Onboarding
  topic: keywords
subject: Autonomous Decision Support in HR Platforms
  topic: keywords
subject: Digital Workforce Transformation
  topic: keywords
creator: Ghatoth Mishra
rights: Copyright (c) 2024
date: 2024-12-27
publication date: 2024-12-27
starting page: 11873
ending page: 11887
doi: 10.15662/IJRAI.2024.0706034

Resilient Middleware and API-Driven Interoperable Platforms for Open Banking and Large-Scale Enterprise Integration
type: item
description: The rapid evolution of digital financial services and enterprise ecosystems has intensified the demand for interoperable, secure, and resilient integration platforms. Open banking initiatives, driven by regulatory frameworks and market competition, require financial institutions to expose standardized application programming interfaces (APIs) to third-party providers while maintaining strict security, compliance, and performance guarantees. Simultaneously, large-scale enterprises must integrate heterogeneous legacy systems, cloud-native applications, microservices, and external partner platforms. These dynamics necessitate robust middleware architectures capable of ensuring interoperability, fault tolerance, scalability, and governance. This research proposes a comprehensive framework for resilient middleware and API-driven interoperable platforms tailored for open banking and enterprise integration scenarios. The architecture integrates service-oriented and event-driven paradigms, API gateways, service meshes, message brokers, identity and access management (IAM), and observability layers. It incorporates resilience patterns such as circuit breakers, bulkheads, retries, idempotency controls, and distributed tracing to ensure system stability under high-load and failure conditions. The proposed model leverages cloud-native principles, container orchestration, and hybrid-cloud connectivity to support horizontal scalability and high availability. API lifecycle management—covering design, versioning, security enforcement, throttling, and monetization—is embedded within the middleware framework. Standards-based interoperability mechanisms such as RESTful APIs, OAuth 2.0 authorization, and JSON-based messaging enable seamless collaboration among banks, fintech firms, and enterprise partners. A methodological evaluation framework assesses system resilience, latency, throughput, fault recovery time, compliance adherence, and scalability. Simulation results indicate that im…
subject: Articles
  topic: section
subject: Open Banking
  topic: keywords
subject: Middleware Architecture
  topic: keywords
subject: API Management
  topic: keywords
subject: Enterprise Integration
  topic: keywords
subject: Microservices
  topic: keywords
subject: Resilience Engineering
  topic: keywords
subject: Interoperability
  topic: keywords
subject: Cloud-Native Platforms
  topic: keywords
creator: Bela Gipp
rights: Copyright (c) 2026
date: 2026-03-02
publication date: 2026-03-02
starting page: 1
ending page: 11
doi: 10.15662/IJRAI.2026.0902001

Integrated Cloud-Native AI and ML Framework for Secure and Compliant Healthcare–Financial Systems
type: item
description: The adoption of cloud-native AI and machine learning (ML) platforms is transforming enterprise systems across healthcare and financial sectors by enabling scalable, data-driven decision-making and secure, efficient communication. Cloud-native architectures allow applications to fully leverage cloud computing benefits, including elasticity, resilience, and service orchestration, while AI and ML provide predictive analytics, automation, and intelligent insights. In healthcare, AI-enabled platforms enhance patient communication, streamline clinical workflows, and support telemedicine services, while ensuring compliance with privacy regulations such as HIPAA. In financial systems, AI and ML facilitate fraud detection, risk management, and real-time transactional analysis. Security by design is critical for these platforms, integrating encryption, authentication, and access control into the architecture from inception to prevent data breaches, ensure compliance, and maintain trust among stakeholders. This research investigates the design, implementation, and evaluation of cloud-native AI and ML enterprise platforms tailored for healthcare communication and financial systems, emphasizing security, scalability, and performance. Simulation and case studies demonstrate that integrating cloud-native AI/ML with security by design improves operational efficiency, ensures secure data exchange, and enhances decision-making capabilities. The study provides guidelines for developing resilient, secure, and intelligent enterprise platforms.
subject: Articles
  topic: section
subject: Cloud-Native Platforms
  topic: keywords
subject: AI
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Enterprise Systems
  topic: keywords
subject: Healthcare Communication
  topic: keywords
subject: Financial Systems
  topic: keywords
subject: Security by Design
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subject: Data Privacy
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subject: Predictive Analytics
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subject: Scalable Architecture
  topic: keywords
creator: Lars Gustav Holmberg
rights: Copyright (c) 2025
date: 2025-10-14
publication date: 2025-10-14
starting page: 13064
ending page: 13074
doi: 10.15662/IJRAI.2025.0805016