International Journal of Advanced Research in Computer Science & Technology(IJARCST)
Zero-Trust–Based Enterprise Cloud AI Architecture for Secure and Privacy-Preserving Healthcare CNN Systems
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
Composable Commerce Architectures: Building Agile Retail Systems
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.
Cost-Aware Cloud Resource Optimization Models for Enterprise Applications
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.
Secure Healthcare Intelligence using AI-Driven Predictive Systems Integrating Fraud Risk Analytics Cybersecurity and MMS with Cloud Computing and Data Warehousing
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
AI- and Machine Learning-Enabled Secure Scalable Cloud Architectures for Network Reliability Privacy Preservation and Energy Optimization
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.
A Governance-Centric AI Framework for Cloud-Native DevOps Automation in Healthcare Applications with Secure Mobile Systems and Network-Enabled Compliance
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.
Zero Downtime Enterprise Modernization of SAP Infrastructure with Intelligent Automation, Encryption, Cloud Integration, and AI-Enabled Predictive Systems for Supply Chain Resilience
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.
Generative AI–Powered Cloud and Machine Learning Architectures for Digital Privacy and Risk Management in Banking and Trade Systems over 5G Networks
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.
A Secure 5G-Enabled Cloud and AI Computing Model for Financial Risk Prediction and Healthcare Intelligence Using Deep Learning
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.
Multiparty Privacy-Preserving AI with SAP-Based Cyber Defense for Healthcare Business Processes in Cloud and 5G
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.
Integrating Fiber Broadband with SAP and AI for Secure Cloud-Based Digital Advertising and Financial Systems
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.
Copyright (c) 2023 International Journal of Advanced Research in Computer Science & Technology(IJARCST)
Data-Driven Innovation Management using Emerging Digital Technologies
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 innovation initiatives, offering a practical framework for managers and decision-makers. The findings reveal that data-driven innovation management is not solely a technological endeavor, but also a strategic and cultural transformation. The ability to harness data effectively depends on organizational readiness, data governance structures, and the ethical use of data. The study concludes that a hybrid approach—blending technological capability with human creativity and strategic foresight—is essential for sustainable innovation outcomes in the digital age. By embracing emerging digital technologies within an integrated innovation framework, organizations can not only respond to rapidly changing market demands but also proactively shape the future through continuous, data-informed innovation.
Serverless Computing Architectures for Business Process Automation
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.
Function-as-a-Service (FaaS)Cloud workflows
Privacy-Preserving Data Management Techniques for Regulatory Compliance
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, reduced risk of data breaches, and enhanced stakeholder trust. The study concludes that privacy-preserving data management is no longer optional but a strategic necessity in the regulatory landscape. As regulations continue to evolve, organizations must adopt adaptive privacy technologies and robust governance models to ensure sustained compliance. This research contributes to a deeper understanding of how privacy-preserving techniques can be systematically implemented to support lawful, ethical, and secure data management practices.
Cyber Risk Assessment Models for Strategic Information Security Management
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
Hybrid Machine Learning Models for Predictive Business Intelligence in Enterprise Systems
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
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 framework contributes to both theory and practice by offering a structured model that aligns AI capabilities with strategic management objectives. It provides a roadmap for organizations seeking to leverage AI for long-term planning, competitive positioning, and sustainable growth. The study concludes that AI-driven decision support systems are no longer optional but essential for strategic management in data-centric organizations operating in volatile and data-intensive environments.
Reinforcement Learning Models for Optimal Resource Allocation in Smart Enterprises
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.
Generative AI for Automated Strategic Planning and Scenario Forecasting
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.
Strategic PlanningDecision Support Systems
Risk-Aware Secure Data Mesh using Databricks for SAP and Healthcare Cloud Platforms
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.
Real-Time Predictive DevOps Intelligence for Risk-Aware Digital Business Processes in Cloud and SAP Ecosystems
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.
AI-Enabled SAP Secure Financial Operations for Credit Risk and Fraud Detection with Regulatory Compliance and Smart City Network Benchmarking
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.
Network Performance in Autonomous Vehicle Communication
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 enable the seamless and efficient exchange of critical data between autonomous vehicles, infrastructure, and cloud-based services, ultimately supporting the safe and successful operation of autonomous driving technology [3-5].
Engineering Large-Scale WMS Integrations: A Practical Guide to Implementing Blue Yonder with IBM ACE, Datapower, MQ, and SAP
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.
Explainable AI-Driven Secure Multi-Modal Analytics for Financial Fraud Detection and Cyber-Enabled Pharmaceutical Network Analysis
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.
Explainable Artificial Intelligence (XAI)
IMPACT OF MATCH RATES ON COST BASIS METRICS IN PRIVACY- PRESERVING DIGITAL ADVERTISING
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].
The Role of Adstock and Saturation Curves in Marketing Mix Models: Implications for Accuracy and Decision-Making
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.
Next Generation Digital Infrastructure Management Using GitOps and AI-Powered Analytics for Secure Financial and Public Services
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.
Modernizing Healthcare Portals Using AI-Enabled Cloud-Native Microservices and SAP-Based Business Processes
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.
A Predictive Analytics–Driven AI Model for Secure and Risk-Aware SAP Healthcare Systems in Cloud Environments
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.