International Journal of Advanced Research in Computer Science & Technology(IJARCST)
type: channel
language: en
publication name: International Journal of Advanced Research in Computer Science & Technology(IJARCST)
items: Zero-Trust–Based Enterprise Cloud AI Architecture for Secure and Privacy-Preserving Healthcare CNN Systems
items: Composable Commerce Architectures: Building Agile Retail Systems
items: Cost-Aware Cloud Resource Optimization Models for Enterprise Applications
items: Secure Healthcare Intelligence using AI-Driven Predictive Systems Integrating Fraud Risk Analytics Cybersecurity and MMS with Cloud Computing and Data Warehousing
items: AI- and Machine Learning-Enabled Secure Scalable Cloud Architectures for Network Reliability Privacy Preservation and Energy Optimization
items: A Governance-Centric AI Framework for Cloud-Native DevOps Automation in Healthcare Applications with Secure Mobile Systems and Network-Enabled Compliance
items: Zero Downtime Enterprise Modernization of SAP Infrastructure with Intelligent Automation, Encryption, Cloud Integration, and AI-Enabled Predictive Systems for Supply Chain Resilience
items: Generative AI–Powered Cloud and Machine Learning Architectures for Digital Privacy and Risk Management in Banking and Trade Systems over 5G Networks
items: A Secure 5G-Enabled Cloud and AI Computing Model for Financial Risk Prediction and Healthcare Intelligence Using Deep Learning
items: Multiparty Privacy-Preserving AI with SAP-Based Cyber Defense for Healthcare Business Processes in Cloud and 5G
items: Integrating Fiber Broadband with SAP and AI for Secure Cloud-Based Digital Advertising and Financial Systems
items: Data-Driven Innovation Management using Emerging Digital Technologies
items: Serverless Computing Architectures for Business Process Automation
items: Privacy-Preserving Data Management Techniques for Regulatory Compliance
items: Cyber Risk Assessment Models for Strategic Information Security Management
items: Hybrid Machine Learning Models for Predictive Business Intelligence in Enterprise Systems
items: https://ijarcst.org/index.php/ijarcst/article/view/363
items: Reinforcement Learning Models for Optimal Resource Allocation in Smart Enterprises
items: Generative AI for Automated Strategic Planning and Scenario Forecasting
items: Risk-Aware Secure Data Mesh using Databricks for SAP and Healthcare Cloud Platforms
items: Real-Time Predictive DevOps Intelligence for Risk-Aware Digital Business Processes in Cloud and SAP Ecosystems
items: AI-Enabled SAP Secure Financial Operations for Credit Risk and Fraud Detection with Regulatory Compliance and Smart City Network Benchmarking
items: Network Performance in Autonomous Vehicle Communication
items: Engineering Large-Scale WMS Integrations: A Practical Guide to Implementing Blue Yonder with IBM ACE, Datapower, MQ, and SAP
items: Explainable AI-Driven Secure Multi-Modal Analytics for Financial Fraud Detection and Cyber-Enabled Pharmaceutical Network Analysis
items: IMPACT OF MATCH RATES ON COST BASIS METRICS IN PRIVACY- PRESERVING DIGITAL ADVERTISING
items: The Role of Adstock and Saturation Curves in Marketing Mix Models: Implications for Accuracy and Decision-Making
items: Next Generation Digital Infrastructure Management Using GitOps and AI-Powered Analytics for Secure Financial and Public Services
items: Modernizing Healthcare Portals Using AI-Enabled Cloud-Native Microservices and SAP-Based Business Processes
items: A Predictive Analytics–Driven AI Model for Secure and Risk-Aware SAP Healthcare Systems in Cloud Environments

Zero-Trust–Based Enterprise Cloud AI Architecture for Secure and Privacy-Preserving Healthcare CNN Systems
type: item
description: The rapid adoption of Convolutional Neural Networks (CNNs) in healthcare has significantly improved diagnostic accuracy, medical image analysis, and clinical decision support. However, deploying CNN-based healthcare AI systems in cloud environments introduces substantial security and privacy risks due to the sensitive nature of patient data and the complexity of distributed infrastructures. Traditional perimeter-based security models are insufficient to address modern cyber threats, insider risks, and regulatory requirements. This paper proposes a zero-trust–based enterprise cloud AI architecture designed to ensure secure and privacy-preserving deployment of healthcare CNN systems. The proposed architecture integrates zero-trust security principles across business, application, data, and infrastructure layers, enforcing continuous authentication, least-privilege access, and real-time monitoring. It incorporates secure data ingestion, cloud-native MLOps pipelines, encrypted model lifecycle management, and compliance-aware governance mechanisms aligned with healthcare regulations such as HIPAA and GDPR. By embedding zero-trust concepts into enterprise architecture, the proposed framework mitigates attack surfaces while maintaining scalability and operational efficiency. This work provides a holistic blueprint for healthcare organizations seeking to operationalize CNN-based AI systems in cloud environments without compromising data confidentiality, integrity, or availability
subject: Articles
  topic: section
subject: Zero Trust Architecture
  topic: keywords
subject: Healthcare AI
  topic: keywords
subject: Convolutional Neural Networks
  topic: keywords
subject: Secure Cloud Computing
  topic: keywords
subject: Privacy Preservation
  topic: keywords
subject: Enterprise Architecture
  topic: keywords
subject: MLOps
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
creator: Ana Patricia Torres
rights: Copyright (c) 2025
date: 2025-09-07
publication date: 2025-09-07
starting page: 12949
ending page: 12956
doi: 10.15662/IJARCST.2025.0805026

Composable Commerce Architectures: Building Agile Retail Systems
type: item
description: With the rapidly changing environment of online shopping, the old monolith systems are not sufficient to serve the speed requirement and scale and individuality need at a high motivated rate. The given paper discusses the disruptive power of composable commerce architectures, i.e., modularization, API-first, cloud-native systems enabling retailers to develop agile, scalable, and resilient retail environments. Based on empirical evidence, industry case studies and architecture review we are going to scrutinise how composable systems excel over legacy systems in the deployment speed, operational efficiency, customer satisfaction and organizational agility. Composable commerce enables functionalities of commerce (checkout, catalog, inventory, and personalization) to be separated, allowing the parallel development of commerce solutions, speed of market, and real-time integration to customer preferences. The paper also identifies the enabler technologies which include, Kubernetes, events-driven microservices and serverless computing which has made composability at scale a reality. Quantitative results indicate up to 53 percent in the speed of feature deployment, 20 percent in cost savings, significant improvements in the measures of developer productivity and customer experience. Although the needs of composable commerce may seem a current problem in terms of integrations and governance, it also can be defined as a legacy strategy to create a future-proof digital commerce framework. Concluding the paper with the recommendations, in which the incremental transformation, cross-functional alignment, and cross-functional alignment of the best practices of the platform engineering are stressed when enterprises switch to the composable models after the monolithic ones.
subject: Articles
  topic: section
subject: Retail
  topic: keywords
subject: Architecture
  topic: keywords
subject: Agile
  topic: keywords
subject: Composable Commerce
  topic: keywords
creator: Sudhakar Bathina
rights: Copyright (c) 2025
date: 2025-06-30
publication date: 2025-06-30
starting page: 12226
ending page: 12231
doi: 10.15662/IJARCST.2025.0803012

Cost-Aware Cloud Resource Optimization Models for Enterprise Applications
type: item
description: Cost-aware cloud resource optimization models aim to intelligently allocate, scale, and schedule computing resources for enterprise applications by balancing performance, availability, and Quality of Service (QoS) requirements with operational cost constraints, using techniques such as predictive analytics, machine learning, and optimization algorithms to minimize cloud expenditure while ensuring efficient utilization and business continuity.
subject: Articles
  topic: section
subject: Cost-aware optimization
  topic: keywords
subject: Cloud resource management
  topic: keywords
subject: Enterprise applications
  topic: keywords
subject: Auto-scaling
  topic: keywords
subject: Quality of Service (QoS)
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Cost efficiency
  topic: keywords
creator: Vijaylaxmi
rights: Copyright (c) 2023
date: 2023-12-15
publication date: 2023-12-15
starting page: 9560
ending page: 9566
doi: 10.15662/IJARCST.2023.0606025

Secure Healthcare Intelligence using AI-Driven Predictive Systems Integrating Fraud Risk Analytics Cybersecurity and MMS with Cloud Computing and Data Warehousing
type: item
description: The healthcare sector is increasingly dependent on digital intelligence to enhance clinical decision-making, optimize operational efficiency, and ensure data security. However, the rapid adoption of electronic health records, cloud platforms, and mobile medical systems (MMS) has exposed healthcare infrastructures to sophisticated cyber threats and financial fraud risks. This study proposes a secure healthcare intelligence framework that integrates AI-driven predictive systems with fraud risk analytics, cybersecurity mechanisms, cloud computing, and data warehousing. The framework leverages machine learning algorithms to predict fraudulent activities, detect anomalies, and support proactive risk management while ensuring compliance with healthcare data protection standards. Cloud-based architectures provide scalable storage and computational resources, while centralized data warehouses enable efficient data integration and real-time analytics. Advanced cybersecurity layers, including encryption, access control, and intrusion detection systems, safeguard sensitive medical and financial data. The proposed approach enhances trust, accuracy, and resilience in healthcare intelligence systems, enabling healthcare organizations to deliver secure, data-driven services. The research highlights the potential of combining artificial intelligence and secure cloud technologies to address emerging challenges in healthcare fraud detection, predictive analytics, and information security
subject: Articles
  topic: section
subject: Healthcare Intelligence
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Fraud Risk Analytics
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Data Warehousing
  topic: keywords
subject: Mobile Medical Systems (MMS)
  topic: keywords
creator: Andrea Giovanni Greco
rights: Copyright (c) 2025
date: 2025-11-11
publication date: 2025-11-11
starting page: 71
ending page: 78
doi: 10.15662/IJARCST.2025.0806813

AI- and Machine Learning-Enabled Secure Scalable Cloud Architectures for Network Reliability Privacy Preservation and Energy Optimization
type: item
description: The rapid adoption of cloud computing has intensified the need for secure, reliable, and energy-efficient architectures capable of supporting large-scale intelligent applications. AI- and machine learning-enabled cloud architectures provide advanced capabilities for adaptive network management, proactive threat detection, and optimized resource utilization. This paper presents a secure and scalable cloud architecture that integrates machine learning models for network reliability enhancement, privacy-preserving mechanisms for sensitive data protection, and intelligent energy optimization techniques. The proposed framework employs predictive analytics to anticipate network failures, anomaly detection models to mitigate cyber threats, and privacy-aware learning approaches to ensure data confidentiality across distributed environments. In addition, energy-efficient scheduling and workload optimization strategies are incorporated to reduce operational costs and environmental impact. The architecture is designed to support heterogeneous workloads and dynamic traffic conditions while maintaining high availability and compliance with security standards. Experimental analysis and architectural evaluation demonstrate improved network resilience, enhanced privacy preservation, and significant gains in energy efficiency, making the proposed solution suitable for next-generation cloud and edge computing ecosystems.
subject: Articles
  topic: section
subject: AI-enabled cloud architecture
  topic: keywords
subject: machine learning
  topic: keywords
subject: network reliability
  topic: keywords
subject: privacy preservation
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: energy optimization
  topic: keywords
subject: scalable systems
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: intelligent resource management
  topic: keywords
subject: cloud computing
  topic: keywords
creator: Charlotte Anne Harris
rights: Copyright (c) 2021
date: 2021-10-07
publication date: 2021-10-07
starting page: 5487
ending page: 5495
doi: 10.15662/IJARCST.2021.0405007

A Governance-Centric AI Framework for Cloud-Native DevOps Automation in Healthcare Applications with Secure Mobile Systems and Network-Enabled Compliance
type: item
description: The rapid adoption of cloud-native architectures, mobile enterprise systems, and AI-driven automation has introduced unprecedented challenges in governance, security, compliance, and operational transparency. Traditional governance models are fragmented, reactive, and insufficient to manage the dynamic, distributed, and autonomous nature of modern enterprise environments. This paper proposes a Unified Governance-Centric Artificial Intelligence (UG-AI) Framework that integrates governance, risk management, compliance (GRC), security, and operational intelligence into a single AI-orchestrated control layer. The framework leverages machine learning, policy-as-code, continuous compliance monitoring, and adaptive risk assessment to ensure secure automation across cloud-native platforms, mobile ecosystems, and network-enabled infrastructures. By embedding governance principles directly into AI decision-making processes, the framework enables proactive compliance enforcement, real-time threat mitigation, and auditable automation workflows. The proposed approach addresses regulatory complexity, data sovereignty, identity management, and cross-platform interoperability while supporting scalability and resilience. This research contributes a conceptual architecture, governance workflow model, and implementation strategy aimed at enhancing enterprise trust, regulatory alignment, and operational efficiency in next-generation digital ecosystems.
subject: Articles
  topic: section
subject: Unified AI Governance
  topic: keywords
subject: Cloud-Native Security
  topic: keywords
subject: Enterprise Automation
  topic: keywords
subject: Mobile System Security
  topic: keywords
subject: Compliance Automation
  topic: keywords
subject: Network Governance
  topic: keywords
subject: Policy-as-Code
  topic: keywords
subject: AI-Driven GRC
  topic: keywords
subject: Zero Trust Architecture
  topic: keywords
creator: Maria Ines Santos
rights: Copyright (c) 2023
date: 2023-09-08
publication date: 2023-09-08
starting page: 9050
ending page: 9058
doi: 10.15662/IJARCST.2023.0605013

Zero Downtime Enterprise Modernization of SAP Infrastructure with Intelligent Automation, Encryption, Cloud Integration, and AI-Enabled Predictive Systems for Supply Chain Resilience
type: item
description: Enterprise modernization is critical for organizations relying on SAP infrastructure to maintain operational continuity, optimize decision-making, and enhance supply chain resilience. This study explores a zero-downtime approach to modernizing SAP systems through the integration of intelligent automation, robust encryption protocols, and cloud-based solutions. Additionally, AI-enabled predictive systems are implemented to anticipate supply chain disruptions, improve responsiveness, and optimize resource allocation. The proposed framework demonstrates that combining automation, security, cloud integration, and predictive analytics can reduce downtime, strengthen operational security, and increase supply chain efficiency. This integrated approach provides a scalable model for enterprises seeking to modernize SAP infrastructure while maintaining high availability and operational resilience.
subject: Articles
  topic: section
subject: SAP Infrastructure
  topic: keywords
subject: Enterprise Modernization
  topic: keywords
subject: Intelligent Automation
  topic: keywords
subject: Encryption
  topic: keywords
subject: Cloud Integration
  topic: keywords
subject: AI
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Supply Chain Resilience
  topic: keywords
creator: Tobias Leon Braun
rights: Copyright (c) 2026
date: 2026-01-20
publication date: 2026-01-20
starting page: 1
ending page: 9
doi: 10.15662/1pwth614

Generative AI–Powered Cloud and Machine Learning Architectures for Digital Privacy and Risk Management in Banking and Trade Systems over 5G Networks
type: item
description: The proliferation of digital banking and global trade systems has dramatically increased the volume and velocity of financial data processed daily. At the same time, this growth has intensified concerns over digital privacy, cybersecurity, and real-time risk management. The integration of Generative Artificial Intelligence (AI) and Machine Learning (ML) within scalable cloud computing architectures presents a transformative opportunity to address these challenges, especially when leveraged over 5G network infrastructures that support high-speed, low-latency communication. This research proposes a comprehensive cloud-native framework that harnesses generative AI, advanced ML models, and multi-tenant cloud platforms to deliver robust risk-aware analytics, privacy-preserving computation, and adaptive security mechanisms for banking and trade systems. The framework supports continuous ingestion of transactional and trade data, applies generative modeling for anomaly detection, and uses machine learning for proactive risk scoring. Using a simulated dataset of banking operations and trade transactions, we demonstrate the framework’s performance in detecting fraudulent activities while preserving user privacy through differential privacy techniques and federated learning. Findings show high accuracy in risk prediction and significant improvements in privacy protection without compromising system responsiveness, illustrating the potential of generative AI–powered cloud architectures in modern financial ecosystems.
subject: Articles
  topic: section
subject: Generative AI
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Digital Privacy
  topic: keywords
subject: Banking Risk Management
  topic: keywords
subject: Trade Analytics
  topic: keywords
subject: 5G Networks
  topic: keywords
creator: Maheshwari Muthusamy
rights: Copyright (c) 2023
date: 2023-07-07
publication date: 2023-07-07
starting page: 8681
ending page: 8690
doi: 10.15662/IJARCST.2023.0604006

A Secure 5G-Enabled Cloud and AI Computing Model for Financial Risk Prediction and Healthcare Intelligence Using Deep Learning
type: item
description: The rapid adoption of 5G networks, cloud computing, and artificial intelligence has enabled the development of intelligent and data-driven applications across financial and healthcare domains. However, the integration of these technologies introduces significant challenges related to security, scalability, latency, and risk management. This paper proposes a secure 5G-enabled cloud and AI computing model that leverages deep learning techniques for financial risk prediction and healthcare intelligence. The proposed architecture integrates cloud-based data processing, 5G-enabled low-latency communication, and deep learning models to analyze large-scale heterogeneous data in real time. Advanced security mechanisms, including encryption, access control, and secure data transmission, are incorporated to protect sensitive financial and medical information. The model enables accurate risk assessment, anomaly detection, and predictive analytics while ensuring data privacy and regulatory compliance. Experimental evaluation demonstrates improved prediction accuracy, reduced response time, and enhanced system reliability compared to traditional cloud-based approaches. The proposed framework offers a scalable and secure solution for next-generation intelligent financial and healthcare systems.
subject: Articles
  topic: section
subject: 5G networks
  topic: keywords
subject: Cloud computing
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Financial risk prediction
  topic: keywords
subject: Healthcare intelligence
  topic: keywords
subject: Cybersecurity
  topic: keywords
creator: Ali Hassan Mohammed
rights: Copyright (c) 2024
date: 2024-03-20
publication date: 2024-03-20
starting page: 10017
ending page: 10024
doi: 10.15662/IJARCST.2024.0702007

Multiparty Privacy-Preserving AI with SAP-Based Cyber Defense for Healthcare Business Processes in Cloud and 5G
type: item
description: The rapid adoption of cloud computing, 5G networks, and data-driven automation in healthcare has intensified the need for secure, privacy-preserving, and compliant artificial intelligence (AI) frameworks. This paper proposes a multiparty privacy-preserving AI architecture integrated with SAP-enabled risk-based cyber defense to support secure healthcare cloud and 5G-enabled MLOps environments. The framework leverages federated learning, secure aggregation, and differential privacy to enable collaborative model training across distributed healthcare stakeholders while ensuring sensitive patient data remains protected. SAP security and risk management capabilities are incorporated to provide continuous threat intelligence, policy enforcement, and real-time risk assessment across cloud, network, and application layers. The proposed architecture addresses key challenges related to data confidentiality, regulatory compliance, adversarial attacks, and operational resilience in healthcare AI pipelines. By aligning privacy-preserving AI techniques with enterprise-grade SAP security analytics, the solution enhances trust, scalability, and auditability in multiparty healthcare ecosystems. The model demonstrates how secure MLOps can be achieved across heterogeneous cloud and 5G infrastructures while maintaining robust cyber defense and governance.
subject: Articles
  topic: section
subject: Privacy-Preserving AI
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Healthcare Business Processes
  topic: keywords
subject: SAP-Based Cyber Defense
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: 5G Networks
  topic: keywords
subject: Secure MLOps
  topic: keywords
creator: Jonas Paul Weber
rights: Copyright (c) 2023
date: 2023-10-07
publication date: 2023-10-07
starting page: 9041
ending page: 9049
doi: 10.15662/IJARCST.2023.0605012

Integrating Fiber Broadband with SAP and AI for Secure Cloud-Based Digital Advertising and Financial Systems
type: item
description: The rapid expansion of fiber broadband networks has enabled high-performance cloud infrastructures that support data-intensive enterprise applications. At the same time, organizations increasingly rely on SAP platforms and artificial intelligence to manage digital advertising and financial operations. Despite these advancements, ensuring data security, system integrity, and intelligent decision-making across cloud environments remains a significant challenge. This paper presents an integrated framework that combines fiber broadband connectivity with SAP systems and AI-driven analytics to support secure, cloud-based digital advertising and financial enterprise systems. The proposed approach leverages high-bandwidth network capabilities to enable real-time data processing, predictive intelligence, and continuous security monitoring. AI models analyze transactional, behavioral, and operational data to detect anomalies and optimize business performance. The framework incorporates robust security controls to protect sensitive financial and advertising data while maintaining scalability and regulatory compliance. Experimental evaluation demonstrates improved system responsiveness, enhanced threat detection accuracy, and more informed business insights compared to conventional cloud architectures. The findings highlight the value of integrating broadband networks, SAP platforms, and AI technologies for building secure and intelligent enterprise cloud solutions.
subject: Articles
  topic: section
subject: Fiber broadband networks
  topic: keywords
subject: SAP cloud systems
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Digital advertising analytics
  topic: keywords
subject: Financial enterprise systems
  topic: keywords
subject: Cloud security
  topic: keywords
subject: Predictive intelligence
  topic: keywords
creator: Lars Hendrik Jansen
rights: Copyright (c) 2023 International Journal of Advanced Research in Computer Science & Technology(IJARCST)
date: 2023-06-20
publication date: 2023-06-20
starting page: 8287
ending page: 8294
doi: 10.15662/IJARCST.2023.0603009

Data-Driven Innovation Management using Emerging Digital Technologies
type: item
description: In the era of digital transformation, organizations are increasingly relying on data-driven approaches to foster innovation and maintain a competitive edge. The integration of emerging digital technologies—such as Artificial Intelligence (AI), Internet of Things (IoT), Big Data Analytics, Cloud Computing, and Blockchain—has fundamentally transformed how innovation is managed across industries. This paper explores how data-driven innovation management (DDIM) is enabling organizations to enhance decision-making processes, identify new market opportunities, streamline operations, and co-create value with stakeholders. It investigates the paradigm shift from intuition-based innovation practices to evidence-based strategies, emphasizing the pivotal role of real-time data and predictive insights in driving innovation success. Through an extensive review of contemporary literature and case studies, this research highlights the strategic implementation of digital tools in the innovation lifecycle—from ideation and prototyping to commercialization and post-launch analysis. It sheds light on the importance of digital platforms and ecosystems in supporting agile, collaborative, and customer-centric innovation models. Additionally, the study examines the organizational and cultural enablers required to embed a data-driven mindset into innovation teams, such as leadership support, cross-functional collaboration, and continuous learning capabilities. Methodologically, the research adopts a qualitative approach, synthesizing insights from interviews with innovation managers, data scientists, and digital transformation leaders. It analyzes how organizations across various sectors—including manufacturing, healthcare, finance, and retail—have utilized data and digital technologies to enhance product development, service delivery, and business model innovation. Furthermore, the research identifies key performance indicators (KPIs) used to measure the effectiveness of data-driven innov…
subject: Articles
  topic: section
subject: Data-Driven Innovation
  topic: keywords
subject: Emerging Technologies
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Innovation Management
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Big Data Analytics
  topic: keywords
subject: IoT
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Strategic Innovation
  topic: keywords
creator: Dr Arpit Jain
rights: Copyright (c) 2026
date: 2026-01-03
publication date: 2026-01-03
starting page: 7
ending page: 11
doi: 10.15662/IJARCST.2026.0901002

Serverless Computing Architectures for Business Process Automation
type: item
description: Serverless Computing Architectures enable scalable, cost-efficient, and agile Business Process Automation by abstracting infrastructure management and leveraging event-driven, function-as-a-service models to orchestrate workflows, integrate enterprise applications, and support real-time, resilient, and automated business operations across dynamic cloud environments.
subject: Articles
  topic: section
subject: Serverless computing
  topic: keywords
subject: Business process automation
  topic: keywords
subject: Function-as-a-Service (FaaS)
  topic: keywords
subject: Function-as-a-Service (FaaS)Cloud workflows
  topic: keywords
subject: Microservices
  topic: keywords
subject: MicroservicesCost optimization
  topic: keywords
creator: Dudigam Ramya
rights: Copyright (c) 2026
date: 2026-01-03
publication date: 2026-01-03
starting page: 1
ending page: 6
doi: 10.15662/IJARCST.2026.0901001

Privacy-Preserving Data Management Techniques for Regulatory Compliance
type: item
description: The exponential growth of data-driven technologies has intensified concerns surrounding personal data protection, confidentiality, and regulatory compliance. Organizations across sectors increasingly rely on large-scale data analytics, cloud computing, and artificial intelligence to drive decision-making, yet these advancements pose significant privacy risks. Regulatory frameworks such as the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and other data protection laws mandate strict controls over data collection, processing, storage, and sharing. In this context, privacy-preserving data management techniques have emerged as essential tools for ensuring regulatory compliance while enabling continued data utilization. This study examines key privacy-preserving data management techniques that help organizations balance regulatory obligations with operational and analytical needs. Techniques such as data anonymization, pseudonymization, encryption, differential privacy, secure multi-party computation, and federated learning are analyzed in terms of their effectiveness, implementation complexity, and compliance alignment. These methods aim to minimize exposure of personally identifiable information (PII) while maintaining data utility and integrity. The paper also explores privacy-by-design and privacy-by-default principles, emphasizing their role in embedding compliance into system architectures from the outset. A mixed-method research approach is adopted, combining qualitative analysis of existing regulatory guidelines and literature with a conceptual evaluation of privacy technologies in real-world compliance scenarios. The findings suggest that no single technique can fully address all regulatory requirements; instead, a layered and hybrid approach is necessary. Organizations that integrate multiple privacy-enhancing technologies within their data governance frameworks demonstrate improved compliance readiness…
subject: Articles
  topic: section
subject: Privacy-preserving data management
  topic: keywords
subject: regulatory compliance
  topic: keywords
subject: data protection
  topic: keywords
subject: anonymization
  topic: keywords
subject: encryption
  topic: keywords
subject: differential privacy
  topic: keywords
subject: data governance
  topic: keywords
creator: Dr Arpit Jain
rights: Copyright (c) 2025
date: 2025-12-15
publication date: 2025-12-15
starting page: 13259
ending page: 13262
doi: 10.15662/IJARCST.2025.0806024

Cyber Risk Assessment Models for Strategic Information Security Management
type: item
description: This paper presents a comprehensive overview of cyber risk assessment models for strategic information security management, focusing on the systematic identification, analysis, and prioritization of cyber threats and vulnerabilities to support informed decision-making, optimize security investments, and enhance organizational resilience in dynamic digital environments
subject: Articles
  topic: section
subject: Cyber Risk Assessment
  topic: keywords
subject: Information Security Management
  topic: keywords
subject: Risk Modeling
  topic: keywords
subject: Threat Analysis
  topic: keywords
subject: Vulnerability Assessment
  topic: keywords
subject: Strategic Decision-Making
  topic: keywords
subject: Cybersecurity Governance
  topic: keywords
creator: Tarang Jain
rights: Copyright (c) 2025
date: 2025-12-15
publication date: 2025-12-15
starting page: 13250
ending page: 13258
doi: 10.15662/IJARCST.2025.0806023

Hybrid Machine Learning Models for Predictive Business Intelligence in Enterprise Systems
type: item
description: Hybrid machine learning models have emerged as a powerful approach for enhancing predictive business intelligence within enterprise systems by combining the strengths of multiple learning techniques. Traditional single-model approaches often struggle with complex, large-scale, and heterogeneous enterprise data, leading to limitations in prediction accuracy and adaptability. This paper explores the use of hybrid machine learning models that integrate methods such as supervised learning, unsupervised learning, deep learning, and ensemble techniques to improve predictive performance and decision support in enterprise environments. The study highlights how hybrid models enable more accurate forecasting, improved pattern recognition, and scalable analytics across enterprise functions. By embedding hybrid machine learning into business intelligence systems, organizations can achieve more robust, flexible, and actionable insights that support data-driven decision-making and strategic planning
subject: Articles
  topic: section
subject: Hybrid machine learning
  topic: keywords
subject: predictive business intelligence
  topic: keywords
subject: enterprise systems
  topic: keywords
subject: data analytics
  topic: keywords
subject: ensemble learning
  topic: keywords
subject: decision support
  topic: keywords
creator: Dr. Musheer Vaqur
rights: Copyright (c) 2024
date: 2024-12-15
publication date: 2024-12-15
starting page: 11427
ending page: 11433
doi: 10.15662/IJARCST.2024.0706031

https://ijarcst.org/index.php/ijarcst/article/view/363
type: item
title: https://ijarcst.org/index.php/ijarcst/article/view/361/352
description: In the era of digital transformation, data-centric organizations increasingly rely on advanced analytics and artificial intelligence (AI) to gain competitive advantage and support strategic decision-making. Traditional decision support systems (DSS), while effective in handling structured data and routine decisions, often lack the adaptability, predictive power, and real-time intelligence required to address complex and dynamic strategic challenges. This study proposes an AI-driven decision support framework tailored for strategic management in data-centric organizations. The framework integrates machine learning, predictive analytics, natural language processing, and knowledge-based systems to enhance the quality, speed, and accuracy of strategic decisions. The proposed framework emphasizes the transformation of raw organizational data into actionable strategic insights through automated data ingestion, intelligent processing, and continuous learning mechanisms. AI models are employed to analyze historical and real-time data, identify patterns, forecast future trends, and simulate alternative strategic scenarios. By embedding explainable AI (XAI) components, the framework ensures transparency and trust in decision outcomes, enabling managers to understand and justify AI-driven recommendations. Additionally, the framework supports collaborative decision-making by combining human expertise with AI-generated insights, fostering a hybrid intelligence approach. The study adopts a conceptual and analytical methodology supported by a review of existing literature on AI-based decision support, strategic management, and data-driven organizations. The findings indicate that AI-driven DSS significantly enhance strategic agility, risk assessment, and resource allocation while reducing cognitive bias and uncertainty in managerial decisions. However, challenges such as data quality, ethical concerns, skill gaps, and organizational resistance are also identified. The proposed fra…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Decision Support Systems
  topic: keywords
subject: Strategic Management
  topic: keywords
subject: Data-Centric Organizations
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Amit Kumar
rights: Copyright (c) 2024
date: 2024-12-15
publication date: 2024-12-15
starting page: 11423
ending page: 11426
doi: 10.15662/IJARCST.2024.0706030

Reinforcement Learning Models for Optimal Resource Allocation in Smart Enterprises
type: item
description: This study explores the application of reinforcement learning (RL) models for optimal resource allocation in smart enterprises, where dynamic, data-driven environments require adaptive and autonomous decision-making. By modeling enterprise resource allocation as a sequential decision process, RL agents learn optimal policies through continuous interaction with operational systems, balancing competing objectives such as cost efficiency, productivity, energy consumption, and service quality. The proposed framework integrates real-time enterprise data, predictive analytics, and feedback-driven learning to enable intelligent allocation of financial, human, and computational resources under uncertainty. Experimental evaluations and simulated enterprise scenarios demonstrate that RL-based approaches outperform traditional rule-based and optimization methods by improving utilization efficiency, responsiveness to demand fluctuations, and long-term strategic performance. The findings highlight the potential of reinforcement learning as a core enabler of self-optimizing, resilient, and sustainable smart enterprises.
subject: Articles
  topic: section
subject: Reinforcement Learning
  topic: keywords
subject: Resource Allocation
  topic: keywords
subject: Smart Enterprises
  topic: keywords
subject: Autonomous Decision-Making
  topic: keywords
subject: Dynamic Optimization
  topic: keywords
subject: Intelligent Systems
  topic: keywords
creator: Dr. Anu Sharma
rights: Copyright (c) 2023
date: 2023-12-15
publication date: 2023-12-15
starting page: 9553
ending page: 9559
doi: 10.15662/IJARCST.2023.0606024

Generative AI for Automated Strategic Planning and Scenario Forecasting
type: item
description: Generative Artificial Intelligence (GenAI) is transforming strategic management by enabling automated strategic planning and advanced scenario forecasting under conditions of uncertainty and complexity. By leveraging large language models, generative simulation, and probabilistic reasoning, organizations can synthesize vast internal and external data sources to generate strategic alternatives, evaluate future scenarios, and support evidence-based decision-making. This approach enhances agility, reduces cognitive bias in planning, and improves foresight by continuously adapting strategies to dynamic market, technological, and geopolitical changes. Generative AI-driven systems thus provide scalable, adaptive, and data-informed strategic intelligence for modern organizations operating in volatile environments.
subject: Articles
  topic: section
subject: Generative AI
  topic: keywords
subject: Strategic Planning
  topic: keywords
subject: Strategic PlanningDecision Support Systems
  topic: keywords
subject: Decision Support Systems
  topic: keywords
subject: Business Intelligence
  topic: keywords
subject: Organizational Strategy
  topic: keywords
creator: Dr. Anu Sharma
rights: Copyright (c) 2023
date: 2023-12-15
publication date: 2023-12-15
starting page: 9546
ending page: 9552
doi: 10.15662/IJARCST.2023.0606023

Risk-Aware Secure Data Mesh using Databricks for SAP and Healthcare Cloud Platforms
type: item
description: The rapid adoption of cloud platforms in SAP-enabled healthcare environments has intensified challenges related to data security, regulatory compliance, scalability, and risk management. Traditional centralized data architectures often struggle to support real-time analytics, cross-domain interoperability, and governance at scale. This paper proposes a Risk-Aware Secure Data Mesh using Databricks for SAP and healthcare cloud platforms, enabling decentralized data ownership while enforcing enterprise-wide security and governance policies. The framework integrates Databricks Lakehouse capabilities with domain-oriented data products, zero-trust security principles, and automated risk controls across networks, APIs, and data layers. Advanced access control, encryption, lineage tracking, and policy-as-code mechanisms are employed to ensure compliance with healthcare regulations and SAP data standards. The proposed architecture supports real-time and batch analytics, improves data reliability, and enhances operational resilience in multi-cloud and hybrid environments. By embedding risk-awareness into data pipelines and analytics workflows, the solution enables secure data sharing, faster insights, and informed decision-making across healthcare business processes.
subject: Articles
  topic: section
subject: Data Mesh
  topic: keywords
subject: Databricks Lakehouse
  topic: keywords
subject: SAP Cloud
  topic: keywords
subject: Healthcare Analytics
  topic: keywords
subject: Risk-Aware Security
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Cloud Architecture
  topic: keywords
creator: Hassan Ahmed Rashid Al-Mazrouei
rights: Copyright (c) 2025
date: 2026-09-15
publication date: 2026-09-15
starting page: 12941
ending page: 12948
doi: 10.15662/IJARCST.2025.0805025

Real-Time Predictive DevOps Intelligence for Risk-Aware Digital Business Processes in Cloud and SAP Ecosystems
type: item
description: The increasing complexity of cloud-native enterprise systems, particularly those integrating SAP platforms and digital business workflows, has introduced significant challenges in maintaining performance, reliability, and risk compliance across DevOps pipelines. Real-time digital business processes—such as online advertising platforms, customer engagement systems, and SAP-driven enterprise applications—demand continuous integration and delivery (CI/CD) pipelines that can proactively anticipate failures, performance degradation, and operational risks. This paper presents a Real-Time Predictive DevOps Intelligence framework that leverages artificial intelligence and data-driven performance forecasting to enable risk-aware automation across CI/CD pipelines in cloud and SAP ecosystems. The proposed approach continuously collects telemetry data from application logs, network metrics, infrastructure monitoring tools, and SAP workloads to train predictive models capable of forecasting pipeline bottlenecks, UI test failures, and deployment risks before they impact production systems. By integrating machine learning–based anomaly detection and performance prediction into DevOps workflows, the framework enables proactive risk mitigation, optimized release decisions, and improved service reliability. Experimental analysis demonstrates that the proposed solution significantly reduces deployment failures, improves mean time to recovery, and enhances operational visibility across hybrid cloud environments. The results highlight the effectiveness of predictive DevOps intelligence in supporting resilient, scalable, and risk-aware digital business operations within modern cloud and SAP-driven enterprises.
subject: Articles
  topic: section
subject: Predictive DevOps Intelligence
  topic: keywords
subject: Real-Time Risk Management
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: SAP Ecosystems
  topic: keywords
subject: Digital Business Processes
  topic: keywords
subject: AI-Driven Analytics
  topic: keywords
subject: CI/CD Automation
  topic: keywords
creator: Dr. M. Rajasekar
rights: Copyright (c) 2024
date: 2024-07-11
publication date: 2024-07-11
starting page: 10713
ending page: 10718
doi: 10.15662/IJARCST.2024.0704016

AI-Enabled SAP Secure Financial Operations for Credit Risk and Fraud Detection with Regulatory Compliance and Smart City Network Benchmarking
type: item
description: The rapid digitalization of financial services within smart city ecosystems has significantly increased the complexity of credit risk assessment, fraud detection, and regulatory compliance. Enterprise Resource Planning (ERP) platforms such as SAP play a pivotal role in managing large-scale financial operations, yet traditional rule-based systems struggle to address evolving fraud patterns, real-time risk evaluation, and cross-regulatory requirements. This paper proposes an AI-enabled SAP secure financial operations framework that integrates machine learning–based credit risk modeling, intelligent fraud detection, regulatory compliance automation, and smart city network benchmarking. The framework leverages advanced analytics, SAP HANA in-memory computing, and AI-driven governance mechanisms to enhance financial security, transparency, and resilience. Experimental results demonstrate improved fraud detection accuracy, reduced credit default risk, enhanced regulatory adherence, and scalable benchmarking across smart city financial networks. The findings highlight the transformative role of AI-driven SAP systems in enabling secure, compliant, and data-driven financial operations in smart city environments.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: SAP Financial Operations
  topic: keywords
subject: Credit Risk Assessment
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
subject: Smart Cities
  topic: keywords
subject: Network Benchmarking
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Financial Security
  topic: keywords
subject: ERP Systems
  topic: keywords
creator: Christophe Julien Gauthier
rights: Copyright (c) 2024
date: 2024-11-13
publication date: 2024-11-13
starting page: 11417
ending page: 11422
doi: 10.15662/IJARCST.2024.0706029

Network Performance in Autonomous Vehicle Communication
type: item
description: Autonomous vehicles are revolutionizing the transportation industry, offering the potential for increased safety, efficiency, and convenience. However, the success of autonomous vehicles depends on reliable and high-performance communication networks that can support the real-time data exchange required for their operation [1, 2]. This research paper examines the key factors influencing network performance in autonomous vehicle communication, including the role of vehicular ad-hoc networks, the challenges posed by high-mobility environments, and the potential of 5G technology to address these challenges. This research paper aims to delve into the factors influencing network performance in autonomous vehicle communication, leveraging stochastic geometry and network layer perspectives to gain insights into the complexities involved. It examines the current state of the art, research challenges, and potential applications, drawing upon recent advancements in wireless communication technology and the car industry. The paper explores the various protocols, communication architectures, and their impact on network performance, highlighting the need for robust and adaptive solutions to cater to the unique requirements of autonomous vehicle networks. As the world shifts towards the widespread adoption of autonomous vehicles, the importance of understanding network performance in the context of vehicle-to-vehicle and vehicle-to-infrastructure communication has become increasingly paramount. The surge of information generated by autonomous vehicles, coupled with the dynamic nature of vehicle density, has presented significant challenges in ensuring reliable and scalable communication. To address these challenges, researchers have explored various approaches, such as trajectory-prediction-based relay schemes and innovations in wireless resource management, to enhance the performance and reliability of communication systems for autonomous vehicles. These advancements can help en…
subject: Articles
  topic: section
subject: Network Performance
  topic: keywords
subject: Autonomous Vehicle
  topic: keywords
subject: VANET
  topic: keywords
subject: Protocols
  topic: keywords
subject: Communication Network
  topic: keywords
creator: Abhishek Singh
rights: Copyright (c) 2024
date: 2024-01-15
publication date: 2024-01-15
starting page: 9712
ending page: 9717
doi: 10.15662/IJARCST.2024.0701006

Engineering Large-Scale WMS Integrations: A Practical Guide to Implementing Blue Yonder with IBM ACE, Datapower, MQ, and SAP
type: item
description: Modern enterprises operating at scale rely heavily on Warehouse Management Systems (WMS) to enable real-time inventory visibility, optimized order fulfillment, and resilient supply-chain operations. As organizations adopt best-of-breed platforms such as Blue Yonder WMS, seamless integration with existing enterprise ecosystems—including ERP systems, middleware platforms, and messaging infrastructures—becomes a critical architectural challenge. Large-scale WMS integrations are often characterized by high transaction volumes, stringent latency requirements, heterogeneous protocols, and complex error-handling scenarios. This article presents a practical, architecture-driven guide to engineering large-scale WMS integrations using Blue Yonder in conjunction with enterprise integration components such as IBM App Connect Enterprise (ACE), IBM DataPower Gateway, IBM MQ, and SAP systems. Rather than focusing on tool-specific configurations, the paper emphasizes reusable integration patterns, message orchestration strategies, security models, and operational best practices that enable scalable, resilient, and maintainable WMS integrations. Key integration flows—including inbound order processing, outbound shipment confirmation, inventory synchronization, and exception handling—are examined from an end-to-end perspective. The article also discusses non-functional considerations such as throughput optimization, fault tolerance, transaction consistency, monitoring, and governance in distributed environments. Through architectural diagrams, sequence flows, and integration patterns, this paper provides a comprehensive blueprint for enterprises seeking to implement or modernize Blue Yonder WMS integrations within complex, hybrid enterprise landscapes.
subject: Articles
  topic: section
subject: Warehouse Management System (WMS)
  topic: keywords
subject: Blue Yonder
  topic: keywords
subject: Enterprise Integration
  topic: keywords
subject: IBM App Connect Enterprise (ACE)
  topic: keywords
subject: IBM DataPower
  topic: keywords
subject: IBM MQ
  topic: keywords
subject: SAP Integration
  topic: keywords
subject: Event-Driven Architecture
  topic: keywords
subject: Middleware
  topic: keywords
subject: Supply Chain Systems
  topic: keywords
subject: Message Orchestration
  topic: keywords
subject: Scalable Integration Architecture
  topic: keywords
creator: Hariprakash Pasumarthi
rights: Copyright (c) 2024
date: 2024-04-10
publication date: 2024-04-10
starting page: 10008
ending page: 10016
doi: 10.15662/IJARCST.2024.0702006

Explainable AI-Driven Secure Multi-Modal Analytics for Financial Fraud Detection and Cyber-Enabled Pharmaceutical Network Analysis
type: item
description: The rapid growth of digital financial transactions and interconnected pharmaceutical supply chains has increased exposure to fraud, cyberattacks, and complex systemic risks. Traditional analytics approaches often struggle to handle heterogeneous data sources while maintaining transparency and trust. This paper proposes an Explainable AI-driven secure multi-modal analytics framework that integrates financial, transactional, network, and textual data to enhance financial fraud detection and cyber-enabled pharmaceutical network analysis. The framework leverages multi-modal big data analytics to capture complex patterns across diverse data types, while advanced machine learning models identify anomalous behaviors and hidden relationships. To address the lack of transparency in black-box models, Explainable AI (XAI) techniques are incorporated to provide human-understandable justifications for predictions, supporting regulatory compliance and informed decision-making. Security mechanisms, including data integrity validation and cyber-threat awareness, are embedded to ensure robustness against adversarial attacks and data manipulation. Experimental analysis demonstrates that the proposed approach improves detection accuracy, interpretability, and trust compared to conventional methods. By unifying explainability, security, and multi-modal analytics, the framework offers a scalable and trustworthy solution for combating financial fraud and extracting actionable insights from pharmaceutical networks in cyber-risk-prone environments.
subject: Articles
  topic: section
subject: Explainable Artificial Intelligence (XAI)
  topic: keywords
subject: Multi-Modal Big Data Analytics
  topic: keywords
subject: Financial Fraud Detection
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Pharmaceutical Network Analysis
  topic: keywords
subject: Secure Data Analytics
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Trustworthy AI
  topic: keywords
creator: Dr.R.Sugumar
rights: Copyright (c) 2025
date: 2025-12-30
publication date: 2025-12-30
starting page: 13239
ending page: 13249
doi: 10.15662/IJARCST.2025.0806022

IMPACT OF MATCH RATES ON COST BASIS METRICS IN PRIVACY- PRESERVING DIGITAL ADVERTISING
type: item
description: This study explores the role of match rates in determining cost basis metrics for randomized controlled trials (RCTs) conducted through Privacy Enhancing Technologies (PETs), such as multi-party computation (MPC), Trusted Execution Environments (TEEs), and cleanroom- based protocols. These methods are used to measure the causal effectiveness of digital advertising campaigns while maintaining data privacy. We specifically analyze how variations in match rates influence incremental Return on Ad Spend (iROAS) and other cost basis metrics, emphasizing the challenges in deriving accurate estimates within privacy- preserving frameworks. Alternative estimators of match rates and their computability under PETs are discussed. Practical issues, including the inability to account for unmatched conversions, and potential proxies for mitigating these limitations, are examined [1][2].
subject: Articles
  topic: section
subject: Privacy enhancing technologies
  topic: keywords
subject: Multi-party computation
  topic: keywords
subject: Causal inference
  topic: keywords
subject: Digital Ad platforms
  topic: keywords
subject: Auction based RTB’s
  topic: keywords
subject: Data encryption and retrieval
  topic: keywords
subject: Randomized control trials
  topic: keywords
creator: Varun Chivukula
rights: Copyright (c) 2020
date: 2020-08-03
publication date: 2020-08-03
starting page: 3400
ending page: 3405
doi: 10.15662/IJARCST.2020.0304003

The Role of Adstock and Saturation Curves in Marketing Mix Models: Implications for Accuracy and Decision-Making
type: item
description: Adstock and saturation curves are fundamental concepts in marketing mix modeling (MMM) that enable marketers to quantify the effects of advertising over time and identify diminishing returns. By modeling the lagged and cumulative impact of marketing spend (adstock) and incorporating nonlinear response functions (saturation curves), MMMs offer actionable insights for optimizing budgets. This paper explores the theoretical foundations and practical applications of adstock and saturation curves in MMMs. Through empirical examples, we illustrate their influence on model outcomes, highlighting how they enhance interpretability, accuracy, and the reliability of marketing ROI estimates. Furthermore, we discuss challenges and best practices in implementing these mechanisms.
subject: Articles
  topic: section
subject: Marketing Mix Modeling
  topic: keywords
subject: Adstock
  topic: keywords
subject: Saturation Curves
  topic: keywords
subject: Advertising Carryover Effects
  topic: keywords
subject: Diminishing Returns
  topic: keywords
subject: Budget Optimization
  topic: keywords
subject: Marketing ROI Attribution
  topic: keywords
creator: Varun Chivukula
rights: Copyright (c) 2024
date: 2024-04-08
publication date: 2024-04-08
starting page: 10002
ending page: 10007
doi: 10.15662/IJARCST.2024.0702005

Next Generation Digital Infrastructure Management Using GitOps and AI-Powered Analytics for Secure Financial and Public Services
type: item
description: The evolution of digital infrastructure management has been driven by the need for greater operational efficiency, security, and agility in mission-critical environments, especially in financial and public service sectors. This paper explores the integration of GitOps—an operational framework built on Git-based declarative configuration and automated delivery—with AI-powered analytics to create a next-generation infrastructure management paradigm. By leveraging Git as the single source of truth alongside continuous reconciliation engines and smart analytics, organizations can achieve robust automation, enhanced observability, and predictive insights. In financial and public services, where regulatory compliance and cybersecurity are paramount, this combined approach can significantly improve incident response, governance, and risk management. This study presents a comprehensive literature review, research methodology, implementation strategies, and evaluation of the advantages and disadvantages of this model. The findings indicate that GitOps, enriched with AI analytics, reduces error rates, accelerates deployment cycles, and strengthens security posture through anomaly detection and adaptive policy enforcement. The results and discussion underscore the practical implications and challenges of adopting this approach. Concluding remarks outline future research directions focused on standardization, explainable AI, and cross-domain applicability.
subject: Articles
  topic: section
subject: GitOps
  topic: keywords
subject: AI-powered analytics
  topic: keywords
subject: digital infrastructure management
  topic: keywords
subject: financial services security
  topic: keywords
subject: public services
  topic: keywords
subject: automation
  topic: keywords
subject: observability
  topic: keywords
subject: compliance
  topic: keywords
subject: cyber resilience
  topic: keywords
creator: Arjun Ramesh
rights: Copyright (c) 2025
date: 2025-12-21
publication date: 2025-12-21
starting page: 13230
ending page: 13238
doi: 10.15662/IJARCST.2025.0806021

Modernizing Healthcare Portals Using AI-Enabled Cloud-Native Microservices and SAP-Based Business Processes
type: item
description: Healthcare organizations are increasingly modernizing legacy portals to improve scalability, interoperability, and user experience while meeting stringent regulatory and operational requirements. This paper presents an AI-enabled cloud-native microservices architecture for healthcare portal modernization with seamless SAP integration. The proposed architecture leverages containerization, service orchestration, and continuous delivery pipelines to enable modular development, rapid deployment, and resilient operations. Artificial intelligence components are integrated for intelligent monitoring, anomaly detection, predictive resource management, and personalized healthcare services. SAP systems are incorporated through secure APIs and event-driven integration patterns to support enterprise workflows, billing, and clinical operations. The architecture enhances system availability, performance, and maintainability while enabling real-time data processing and analytics. Experimental evaluation demonstrates improved deployment efficiency, fault isolation, and operational visibility compared to monolithic architectures, making the approach suitable for large-scale healthcare enterprises.
subject: Articles
  topic: section
subject: Cloud-native microservices
  topic: keywords
subject: Healthcare portal modernization
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: SAP integration
  topic: keywords
subject: Continuous delivery
  topic: keywords
subject: Container orchestration
  topic: keywords
subject: Enterprise healthcare systems
  topic: keywords
creator: André Roberto Pinto
rights: Copyright (c) 2025
date: 2025-12-15
publication date: 2025-12-15
starting page: 13223
ending page: 13229
doi: 10.15662/IJARCST.2025.0806020

A Predictive Analytics–Driven AI Model for Secure and Risk-Aware SAP Healthcare Systems in Cloud Environments
type: item
description: The digital transformation of healthcare organizations has led to the widespread adoption of SAP systems deployed in cloud environments to support critical clinical, administrative, and financial operations. While cloud-based SAP platforms offer scalability and agility, they also introduce complex security risks and compliance challenges associated with sensitive healthcare data. This paper proposes a predictive analytics–driven AI model for enabling secure and risk-aware SAP healthcare systems in cloud environments. The proposed model leverages machine learning techniques to analyze historical system logs, access patterns, configuration changes, and operational metrics in order to proactively predict security threats, performance anomalies, and compliance risks. By integrating predictive intelligence into cloud security controls, the framework supports early risk detection, automated alerting, and informed decision-making for system administrators. Experimental evaluation demonstrates improved risk prediction accuracy, reduced incident response time, and enhanced security posture compared to traditional reactive security approaches. The results indicate that AI-driven predictive analytics can significantly strengthen security, resilience, and operational reliability of SAP systems in healthcare cloud ecosystems.
subject: Articles
  topic: section
subject: Predictive Analytics
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: SAP Healthcare Systems
  topic: keywords
subject: Risk Awareness
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Compliance
  topic: keywords
creator: Maheshwari Muthusamy
rights: Copyright (c) 2023
date: 2023-11-10
publication date: 2023-11-10
starting page: 9540
ending page: 9545
doi: 10.15662/IJARCST.2023.0606022