International Journal of Advanced Research in Computer Science & Technology(IJARCST)
Adaptive Enterprise Computing Framework Using Intelligent AI Agents SAP Systems and Hybrid Multi Cloud Architecture
The rapid evolution of Artificial Intelligence (AI), cloud computing, and enterprise digital transformation has significantly changed the way organizations manage business operations and enterprise resource planning systems. Modern enterprises increasingly rely on SAP platforms, intelligent automation, predictive analytics, and hybrid multi-cloud environments to improve operational efficiency, cybersecurity, scalability, and business agility. However, traditional enterprise architectures continue to experience limitations such as fragmented data management, isolated business applications, manual workflows, inconsistent security policies, and limited interoperability among distributed cloud services. These challenges hinder real-time decision-making, intelligent resource optimization, and adaptive business operations. This paper proposes an Adaptive Enterprise Computing Framework that integrates Intelligent AI Agents, SAP enterprise systems, and Hybrid Multi-Cloud Architecture to establish a secure, scalable, and autonomous enterprise ecosystem. The proposed framework utilizes Agentic AI, Generative AI, machine learning, predictive analytics, intelligent automation, and cloud-native technologies to continuously monitor enterprise activities, optimize SAP business processes, automate operational workflows, and strengthen enterprise cybersecurity. Intelligent AI agents collaboratively perform reasoning, planning, security monitoring, predictive analysis, and workflow orchestration while interacting with SAP S/4HANA and cloud-based enterprise applications. Hybrid multi-cloud deployment enables dynamic workload distribution across private and public cloud infrastructures while ensuring high availability, regulatory compliance, disaster recovery, and business continuity. The proposed architecture incorporates Zero Trust Security, Identity and Access Management, DevSecOps, MLOps, container orchestration, and real-time enterprise monitoring to provide adaptive computing capabilities. Experimental analysis demonstrates that the proposed framework significantly improves automation efficiency, enterprise scalability, cybersecurity resilience, decision accuracy, cloud resource utilization, and operational performance compared with conventional enterprise architectures. The framework offers a comprehensive roadmap for organizations seeking intelligent, secure, and adaptive enterprise transformation through AI-enabled computing and hybrid multi-cloud technologies.
Autonomous AI Governance and Cost Optimization Strategies for Multi-Tenant Enterprise and Data Platforms
The rapid adoption of autonomous artificial intelligence (AI) in enterprise ecosystems has transformed the operational dynamics of multi-tenant enterprise and data platforms. These platforms support multiple organizations, business units, and applications on shared infrastructure, enabling scalability, efficiency, and resource pooling. However, the increasing autonomy of AI systems introduces complex challenges in governance, compliance, transparency, security, and cost management. Autonomous AI systems dynamically manage workloads, allocate computing resources, and optimize operations, but without strong governance frameworks, they may lead to inefficiencies, regulatory violations, and financial overruns. This paper examines autonomous AI governance and cost optimization strategies designed for multi-tenant enterprise environments. It explores how AI lifecycle governance, policy enforcement, explainable AI (XAI), and compliance automation can ensure responsible AI operations. Additionally, it investigates cost optimization mechanisms including predictive scaling, intelligent workload scheduling, FinOps integration, serverless architectures, and resource utilization analytics. The study emphasizes the integration of governance and financial optimization as a unified framework rather than separate operational concerns. A qualitative and conceptual research methodology is employed, drawing insights from academic literature, industry reports, and enterprise best practices. The findings highlight that organizations implementing integrated governance and cost optimization frameworks achieve improved operational efficiency, reduced infrastructure costs, enhanced regulatory compliance, and greater system resilience in multi-tenant AI-driven environments.
AI-Driven Intelligent Enterprise Platforms for Secure Cloud Computing SAP Cybersecurity DevOps Automation and Predictive Operational Intelligence
The rapid evolution of enterprise computing has significantly transformed organizational operations through cloud computing, artificial intelligence (AI), cybersecurity automation, SAP digital transformation, and DevOps integration. Modern enterprises increasingly depend on intelligent platforms capable of delivering scalable infrastructure, automated security monitoring, predictive operational intelligence, and real-time business analytics. AI-driven enterprise platforms combine machine learning algorithms, cloud-native architectures, cybersecurity frameworks, SAP enterprise resource planning systems, and DevOps automation to create secure, resilient, and highly efficient business environments. These intelligent ecosystems continuously monitor operational activities, detect anomalies, predict system failures, automate software deployment, and optimize enterprise decision-making while maintaining regulatory compliance and data privacy. Cloud computing further enhances organizational agility by providing elastic resource allocation, cost optimization, and seamless collaboration across geographically distributed infrastructures. Simultaneously, predictive analytics enables proactive maintenance, workload forecasting, and intelligent resource management to improve operational efficiency. This study investigates the integration of AI, secure cloud computing, SAP technologies, cybersecurity automation, DevOps methodologies, and predictive intelligence within enterprise platforms. The research analyzes architectural components, implementation methodologies, operational benefits, and existing challenges associated with intelligent enterprise ecosystems. The proposed framework demonstrates how AI-driven automation strengthens organizational resilience, enhances cybersecurity, accelerates software delivery, and supports data-driven strategic decision-making across modern digital enterprises.
Intelligent Decision Support Systems for Enterprise Modernization using Generative AI Predictive Modeling and Process Automation
learning algorithms, and generative AI capabilities to transform raw enterprise data into actionable insights, automated recommendations, and adaptive workflows. Generative AI enhances decision support by producing contextual insights, scenario simulations, and natural language explanations, enabling decision-makers to interpret complex data more effectively. Predictive modeling further strengthens these systems by forecasting trends, identifying risks, and optimizing resource allocation across business functions. Process automation, including robotic process automation and intelligent workflow orchestration, ensures seamless execution of decisions with minimal human intervention. Together, these technologies create a unified framework for intelligent enterprise modernization that improves responsiveness, reduces operational inefficiencies, and supports data-driven strategic planning. However, challenges such as model transparency, data governance, integration complexity, and ethical concerns remain significant barriers. This study explores the architectural and conceptual foundations of intelligent decision support systems in enterprise environments. A qualitative research methodology based on systematic literature review and conceptual synthesis is employed. Findings indicate that integrating generative AI, predictive modeling, and automation significantly enhances enterprise decision intelligence and modernization outcomes
AI-Driven Secure Cloud Transformation Integrating Governance Automation Observability Healthcare Intelligence and Enterprise Data Systems
Artificial Intelligence (AI) is transforming cloud computing by enabling intelligent automation, enhanced security, real-time monitoring, and advanced analytics across enterprise environments. The integration of AI-driven governance automation, observability platforms, healthcare intelligence systems, and enterprise data architectures has emerged as a critical strategy for achieving secure cloud transformation. Organizations increasingly rely on cloud infrastructures to manage large-scale data, streamline operations, and improve decision-making processes. However, challenges related to security, compliance, data privacy, and operational complexity necessitate advanced AI-based solutions. This study explores how AI technologies support secure cloud transformation through automated governance frameworks, predictive observability mechanisms, healthcare data intelligence, and integrated enterprise data systems. Governance automation ensures regulatory compliance and policy enforcement, while observability platforms provide continuous monitoring and anomaly detection. In healthcare environments, AI-driven cloud systems facilitate clinical decision support, patient analytics, and secure data sharing. Enterprise data systems benefit from intelligent data integration, processing, and management capabilities that enhance organizational efficiency. The research highlights the benefits, challenges, and implementation considerations associated with AI-enabled cloud ecosystems. Findings indicate that combining AI with cloud security and data governance significantly improves operational resilience, scalability, and business intelligence. The study concludes that AI-driven secure cloud transformation represents a sustainable framework for modern digital enterprises seeking innovation, compliance, and long-term competitive advantage.
Reimagining Enterprise Transformation with Cloud Intelligence Autonomous Systems and Resilient Operations Frameworks
Enterprise transformation has become a strategic necessity in the digital era, driven by rapid technological advancements, evolving customer expectations, and increasing market uncertainties. Organizations are increasingly leveraging cloud intelligence, autonomous systems, and resilient operations frameworks to enhance operational efficiency, agility, and business continuity. Cloud intelligence integrates artificial intelligence, machine learning, and cloud computing capabilities to provide scalable data-driven insights that support informed decision-making. Autonomous systems automate routine and complex processes, reducing human intervention while improving productivity, accuracy, and responsiveness. Simultaneously, resilient operations frameworks enable enterprises to withstand disruptions, adapt to changing environments, and maintain continuous service delivery. This study explores the interconnected role of these technologies in facilitating enterprise transformation. It examines how cloud-based intelligent platforms support innovation, how autonomous systems optimize operational workflows, and how resilience-oriented frameworks strengthen organizational sustainability. Through a comprehensive review of existing literature and a conceptual research methodology, the study highlights the strategic benefits, implementation challenges, and future implications of integrating these technologies. The findings suggest that organizations adopting cloud intelligence, autonomous automation, and resilient operational practices achieve greater flexibility, enhanced customer experiences, improved risk management, and long-term competitive advantage. The study contributes to the growing body of knowledge on digital transformation and provides practical insights for enterprises seeking sustainable growth in an increasingly dynamic business environment.
Real-Time Risk Analytics with Data Engineering Pipelines
Risk analysis in real-time is now a staple in the contemporary organization with focus being observed in the financial sector, health sector, and the manufacturing unit. Because of the latest tools in data engineering, large data sets can be analyzed and quickly red flagged in case of risks. This approach entails the ability to take stock of data and then blend, integrate and make sense of it through the use of rapidly advancing technology tools including stream processing, cloud computing, and machine learning, among others. Through these pipelines, risks are prevented in advance, losses are reduced and optimum performance is created by organizations. Based on the analysis of the real-time applications, problems, and perspectives of risk analytics, this paper elaborates real-time risk analytics with detail information.
Orchestrating the Future Autonomous Healthcare Data Pipeline Management through Agentic AI Architectures
Data is undoubtedly the most relevant asset in the digital transformation of businesses. In healthcare, data pipelines enable the automated orchestration and optimization of data flow among diverse systems and stakeholders. Properly designed, they provide a mechanism to abstract and fulfil the resource-related needs of the processes they support, including ingestion, analysis, sharing, and publishing. Nevertheless, healthcare data pipelines are still implemented manually or with ad-hoc automation, which threatens their reliability, flexibility, and efficiency. Integrating Agentic AI (Artificial Intelligence) appears to be the solution for their automation and optimization. Agentic AI systems reproduce the autonomous, self-organizing, decision-making, and multi-role properties of human archaeogenic agentic behaviours. In the context of healthcare data pipelines, these properties enable automatic data ingestion and sharing whenever and wherever needed;participation as data providers, consumers, or orchestrators; dynamic adaptation to changes in resource-related needs, costs, or availability; and multi-agent collaboration with associated role allocation Supporters of this approach claim that Agentic AI will favour businesses in their race towards becoming data-driven by allowing managers to focus on their core business and outsource resource-related problems to self-organizing control loops operating over business processes. Nevertheless, Agentic AI systems have yet to be implemented and tested. This gap deserves attention, especially in a domain as heterogeneous and complex as healthcare, where patients' lives depend on the accurate and reliable availability of data. The following presents an in-depth analysis of healthcare data pipelines and identifies how Agentic AI can support their automation and optimization
Predictive Healthcare Administration Using Advanced Payer Analytics and Population Health Data Engineering
Healthcare systems around the world are struggling to control the costs of healthcare expenditures while maintaining or improving health outcomes for their constituents. Advanced analytics is essential to ascertain upcoming high-risk, high-cost populations. The incorporation of predictive information into payer management decisions presents exciting possibilities for improved budget allocation and patient care outcomes, yet the experimentation and adoption of these emerging techniques in a payer context remain limited. Research gaps identified through a review of academic literature provide the foundation for the subsequent development and demonstration of a comprehensive set of predictive analytics for a fictional payer, MedicarePlus. Data sources are integrated and engineered within a dedicated population health data engineering platform and subsequently exploited via predictive healthcare administration techniques focusing on advanced payer analytics and population health-centered predictive data models. Results demonstrate significant potential for improved mission fulfillment through multiple predictive models, paving the way for expanded predictive capabilities and ensuing value delivery to the citizens of New Hampshire.
Connected Fleet Intelligence: Edge-Centric Analytics and Computer Vision for Predictive Manufacturing and Asset Resilience
Data fusion, edge analytics, computer vision, and predictive manufacturing converge to enhance asset resilience through low-latency, trustworthy decision making. The transition from a reactive to predictive approach to maintenance of industrial assets such as machinery and equipment presents an opportunity to improve asset resilience and avoid unscheduled production downtimes with significant cost implications. Addressing three interrelated challenges—identifying the optimal frequency for maintenance activities; defining asset reliability indicators; and automatic detection of defects—contributions are made to applied predictive maintenance from standalone visual inspection systems in addition to data-driven insights. The Delmia Ortems neural network-based model predicting optimal spare parts ordering for an automotive plant is used. The condition monitoring dataset from the NASA C-MAPSS record set serves as an example to define remaining useful life indicators of turbines and their bearings monitored by vibration sensors. A use case presents a crowdsourced computer vision application for visual defects on city assets. Evidence-based framing encompasses data governance with a business glossary, maturity assessment, and explainability, ensuring stakeholders comprehend, trust, and remain involved with the developed solutions. Reliability indicators assist industrial asset portfolios with both dashboard-style visualization of status and remaining service time versus maintenance. Integration of Vision AI within City Asset Integrity involves a visual damage report from citizens supported by edge-centric computer vision processing and potential triangulation-based coordinates for the defects detected. All solutions are designed for low-latency deployment.
Learning Health Systems Machine Intelligence for Clinical Prediction and Healthcare Optimization
Learning health systems (LHSs) continually and automatically seek ways to improve healthcare delivery and outcomes through a real-time feedback loop. The LHS paradigm encourages the integration of data-driven learning into everyday clinical practice, informing the routine evaluation and refinement of clinical prediction models and optimizing the allocation of resources and workflows. The LHS concept has gained increasing prominence and is now emerging as a mature domain of research and practice. Three factors have contributed to this evolution. First, the enormous volume of readily available patient data, accumulated through decades of routine clinical practice, has enabled machine learning and artificial intelligence techniques to be successfully applied for predicting patient trajectories, risks, and outcomes at multiple time-scales; indeed, patient trajectories are now being predicted at a more granular scale than ever before. Second, health systems are continually seeking ways to allocate scarce resources more intelligently, both for planning purposes and to meet real-time demand; such work can now be informed by the predictions of patient arrivals at an operational level, as well as by predictions of the expected trajectories of patients within the care delivery process. Third, as the introduction of intelligent decision support systems becomes more common in various aspects of healthcare delivery, research is emerging on how best to integrate such decision support systems with clinical teams for practical impact.
Generative AI and Cryptographic Validation Techniques for Secure High-Quality Enterprise Integration Architectures
The rapid growth of enterprise digital transformation has increased the complexity of integrating heterogeneous systems, cloud platforms, Internet of Things (IoT) devices, and business applications. Generative Artificial Intelligence (GenAI) has emerged as a transformative technology that enhances enterprise integration architectures by automating workflow generation, data mapping, API orchestration, anomaly detection, and intelligent decision support. However, the widespread adoption of AI-driven integration introduces significant security concerns, including data manipulation, unauthorized access, model poisoning, and integrity violations. Cryptographic validation techniques provide a robust mechanism for ensuring trustworthiness, confidentiality, authenticity, and non-repudiation across enterprise ecosystems. This study examines the role of Generative AI and cryptographic validation techniques in developing secure and high-quality enterprise integration architectures. The research explores how encryption algorithms, digital signatures, hash functions, blockchain-based validation, and zero-knowledge proof mechanisms can be combined with AI-driven integration frameworks to enhance security and reliability. Furthermore, the study investigates architectural models that leverage AI-generated integration artifacts while maintaining compliance with enterprise security standards. The findings suggest that integrating cryptographic validation with Generative AI improves data integrity, operational transparency, interoperability, and system resilience. The proposed framework offers organizations a scalable and secure approach to managing increasingly complex enterprise environments while ensuring high-quality service delivery and regulatory compliance.
From Predictive Intelligence to Autonomous Enterprise Operations Using AI Cloud and Data Engineering
The evolution of enterprise systems has shifted from traditional analytics-driven decision support to predictive intelligence and, more recently, toward autonomous enterprise operations. This transformation is powered by the convergence of artificial intelligence (AI), cloud computing, and advanced data engineering practices. Predictive intelligence enables organizations to anticipate outcomes using historical and real-time data, while autonomous operations extend this capability by allowing systems to self-monitor, self-heal, and self-optimize with minimal human intervention. Cloud platforms provide scalable infrastructure and computational power necessary for training and deploying AI models at scale, whereas modern data engineering pipelines ensure seamless ingestion, transformation, and governance of massive heterogeneous datasets. This paper explores the conceptual and technical transition from predictive systems to autonomous enterprises, highlighting architectural frameworks, machine learning pipelines, and AI-driven operational intelligence. It also examines how organizations leverage cloud-native tools, data lakes, and streaming analytics to enable real-time decision-making. Furthermore, the study investigates challenges such as data privacy, model drift, integration complexity, and governance risks. By synthesizing current research and industry practices, this paper presents a holistic view of how AI-driven cloud ecosystems are reshaping enterprise operations into intelligent, adaptive, and autonomous systems capable of continuous learning and optimization.
Enterprise Intelligence Frameworks for Self-Governing Platforms Using AI-Driven Decision Systems and Modern Cloud Ecosystems
The emergence of artificial intelligence (AI), cloud computing, and autonomous digital ecosystems has transformed the operational capabilities of modern enterprises. Self-governing platforms represent a new generation of intelligent systems capable of making autonomous decisions, adapting to environmental changes, and optimizing organizational processes with minimal human intervention. Enterprise intelligence frameworks serve as the foundation for integrating AI-driven decision systems with modern cloud ecosystems, enabling organizations to achieve agility, scalability, and operational efficiency. This study explores the design and implementation of enterprise intelligence frameworks that support self-governing platforms through advanced analytics, machine learning, automation, and cloud-native architectures. AI-driven decision systems utilize predictive, prescriptive, and cognitive analytics to continuously monitor business environments, identify patterns, and recommend optimal actions. Modern cloud ecosystems provide the computational resources, storage capabilities, and distributed services necessary to support real-time intelligence generation. The research examines the interaction between autonomous decision-making mechanisms, cloud infrastructure, and enterprise governance models. Additionally, it investigates key technologies such as machine learning, reinforcement learning, intelligent agents, edge computing, and cloud orchestration platforms. Findings suggest that integrating AI-driven decision systems with cloud-based enterprise intelligence frameworks enhances organizational responsiveness, improves decision accuracy, reduces operational costs, and supports sustainable digital transformation. These frameworks are expected to play a critical role in shaping future intelligent enterprises.
Architecting Self-Governing Digital Enterprises with Intelligent Analytics and Secure Cloud Platforms
The rapid evolution of digital technologies has transformed traditional business operations into highly interconnected and data-driven ecosystems. Self-governing digital enterprises represent the next stage of organizational evolution, where intelligent systems autonomously monitor, analyze, and optimize business processes with minimal human intervention. This research explores the architectural foundations of self-governing enterprises by integrating intelligent analytics and secure cloud platforms. Intelligent analytics utilizes artificial intelligence, machine learning, predictive modeling, and real-time data processing to support autonomous decision-making and operational efficiency. Secure cloud platforms provide scalable infrastructure, robust cybersecurity mechanisms, and seamless integration capabilities necessary for enterprise-wide digital transformation. The study examines how organizations can design adaptive architectures that enable continuous learning, automated governance, proactive risk management, and data-driven innovation. Furthermore, the research highlights the role of cloud-native technologies, zero-trust security frameworks, and intelligent automation in creating resilient digital ecosystems. Through a comprehensive review of existing literature and methodological analysis, the study identifies critical success factors, implementation challenges, and future opportunities associated with self-governing enterprises. The findings suggest that the convergence of intelligent analytics and secure cloud environments significantly enhances organizational agility, operational excellence, compliance management, and strategic competitiveness in the digital economy while supporting sustainable and scalable business growth
SECURE ENCLAVE–DRIVEN AI INFRASTRUCTURE: PROTECTING SENSITIVE MODELS AND DATA IN DISTRIBUTED SYSTEMS
The rapid adoption of artificial intelligence (AI) across finance, healthcare, government, and enterprise platforms has intensified concerns around protecting sensitive data, proprietary models, and inference workloads. Traditional security controls such as encryption in transit and at rest are no longer sufficient to safeguard AI pipelines operating in distributed and multi-tenant cloud environments. Confidential computing, powered by secure enclaves and hardware-based trusted execution environments (TEEs), is emerging as a critical approach to protect data while it is actively being processed. This article explores the design and implementation of secure enclave–driven AI infrastructure for distributed systems. It examines how secure enclaves enable confidential model training, privacy-preserving inference, secure data sharing, and trustworthy collaboration across untrusted networks. The paper presents a reference architecture that integrates secure enclaves with container orchestration, federated learning, and zero-trust security models. Additionally, it analyzes key challenges including performance overhead, attestation complexity, key management, and scalability in hybrid and multi-cloud deployments. Through architectural diagrams, comparative tables, and practical design patterns, this study provides guidance for building resilient AI platforms that protect intellectual property and sensitive datasets without sacrificing scalability or performance. The article concludes by outlining future trends in confidential AI, including encrypted machine learning, secure multi-party computation, and regulatory-driven privacy frameworks.
Next-Generation AI and Cloud Computing Architectures for Secure Enterprise Analytics and Fraud Intelligence Platforms
The rapid advancement of artificial intelligence (AI) and cloud computing technologies has transformed enterprise analytics and fraud intelligence systems across multiple industries. Organizations increasingly depend on intelligent digital infrastructures to process massive volumes of data, detect fraudulent activities, and ensure secure business operations in real time. Traditional enterprise systems face limitations in scalability, adaptability, and cybersecurity resilience when dealing with sophisticated fraud attacks and evolving cyber threats. This research explores next-generation AI and cloud computing architectures designed to support secure enterprise analytics and fraud intelligence platforms. The study examines the integration of machine learning, deep learning, edge computing, hybrid cloud environments, blockchain security mechanisms, and zero-trust architectures for improving fraud detection efficiency and enterprise cybersecurity. Advanced AI-driven analytics frameworks enable organizations to identify anomalies, predict risks, and automate security responses using real-time data processing techniques. Cloud-native infrastructures provide scalability, flexibility, and distributed computing capabilities essential for modern enterprise operations. The research further investigates security challenges, regulatory compliance requirements, and privacy-preserving technologies associated with enterprise cloud ecosystems. The findings demonstrate that integrating AI technologies with secure cloud architectures significantly enhances fraud prevention, operational efficiency, data protection, and intelligent decision-making in dynamic enterprise environments
Effect of Yoga and Meditation on Heart Rate Variability: A Comprehensive Review
Heart Rate Variability (HRV) is a robust, non-invasive biomarker of autonomic nervous system function and cardiovascular health. This comprehensive review synthesizes evidence from randomized controlled trials, longitudinal cohort studies, and meta-analyses published between 2000 and 2024 to evaluate the effects of yoga and meditation on HRV indices in diverse populations. Our analysis encompasses 47 peer-reviewed studies involving over 3,200 participants. Findings consistently indicate that yoga and meditation improve HRV parameters, reflecting enhanced parasympathetic tone and reduced sympathetic dominance. RMSSD improvements ranged from 12% to 38% across studies. Pooled Cohen's d = 0.61 (medium-to-large effect). Implications for clinical practice, mental health, and mechanistic research are discussed. The expanded review additionally covers implementation strategies, rehabilitation relevance, future research priorities, and population-specific considerations that position HRV as a practical endpoint in integrative medicine
A. Jeevani Selvabala, R.Madhu Keerthana, K. Ramalingam, J.Ananth, M.Krishna Kumar
High Performance AI Driven Infrastructure Architectures for Scalable Digital Enterprise Systems
High-performance AI-driven infrastructure architectures are transforming scalable digital enterprise systems by enabling intelligent automation, adaptive resource management, real-time analytics, and efficient computational performance across modern digital ecosystems. The rapid growth of cloud computing, big data, Internet of Things (IoT), edge computing, and enterprise applications has significantly increased the demand for scalable and resilient infrastructure solutions capable of supporting dynamic workloads and complex operational requirements. Traditional enterprise infrastructures often face challenges related to scalability, latency, resource optimization, fault tolerance, and operational complexity. Artificial Intelligence (AI) technologies provide advanced capabilities for addressing these challenges through predictive analytics, autonomous management, intelligent orchestration, and self-optimizing operational frameworks. This study examines high-performance AI-driven infrastructure architectures designed for scalable digital enterprise systems. The research explores the integration of AI with cloud-native computing, distributed systems, container orchestration, intelligent networking, and automated infrastructure management platforms. It also analyzes the role of machine learning, deep learning, and predictive analytics in improving operational efficiency, system reliability, cybersecurity, and enterprise scalability. Furthermore, the study investigates implementation challenges such as computational overhead, integration complexity, data privacy concerns, and infrastructure costs. The findings indicate that AI-driven infrastructure architectures significantly enhance enterprise performance, resource utilization, operational resilience, and business continuity, making them essential for future intelligent digital transformation initiatives and next-generation enterprise computing environments.
Autonomous Clinical Monitoring Platforms Using Reinforcement Learning and Deep Neural Networks
Clinical monitoring systems operate independently, but accuracy may suffer because intelligent input validation and alarm justification are not included. Following the architecture of a human-centric reinforcement learning framework, a clinical monitoring platform for hospital wards that uses deep neural networks as foundational components has been constructed. The platform can adapt to any clinical environment through real-time monitoring, context awareness of the physiological condition of patients, and online learning from actual mistakes. Hospital-acquired conditions remain major issues of quality in health-care delivery. These complications, at least in part, are related to the complexity of the clinical environment, which can be difficult to oversee. Autonomous systems can help close that supervisory gap, but existing clinical monitoring systems tend to operate independently. Although these systems are multivariable, their focus remains on detecting anomalies in the monitored signals and warning medical personnel in case something goes wrong. However, these alarms do not consider the operation of other clinical systems, which might justify an alarm without a real need—the so-called crying wolf. Reinforcement learning algorithms, at least in theory, are able to learn to achieve a goal by shaping their behaviour with the help of other intelligent agents operating in the same environment. To achieve this goal in the hospital ward environment, an architectural framework has been developed. The basic assumption is that patients in hospital wards should be in stable physiological conditions. The main task of the proposed autonomous clinical monitor is, therefore, to prevent deterioration of patients’ conditions. Although all monitored signals are relevant, the mission is not to detect every single anomaly but to provide intelligent supervision of all hospital clinical monitoring, telecommunications and warning systems. Most importantly, the proposed system is autonomous: it is able to monitor the context information of the environment and, on the basis of its physiological state, to shape the error feedback, both confirming and invalidating alarms raised by other systems. The entire process is conducted in real time. The developed platform is capable of complementing the current clinical monitoring systems autonomously, learning on the basis of its own mistakes, and its effectiveness has been verified in a closed-loop pipeline by combining it with state-of-the-art clinical monitoring, wireless telecommunication and alarm systems.
Agentic AI-Driven Smart Supply Chain Orchestration for Paint Manufacturing and Retail Ecosystems
In modern supply chains, emerging technologies create unprecedented opportunities for organizations to operate smarter by better orchestrating end-to-end processes across their ecosystems Intelligent decision-making that spans across multiple functions such as fulfillment, manufacturing, sourcing, and inventory management can help achieve complex business objectives not possible at a function level. Fulfilling such objectives calls for traditional supply chain technologies to improve operational efficiency while overcoming limitations related to factors such as end-consumer demand, transit times, product demand patterns, and possible production non-compliance. This scholarly work presents a comprehensive architectural framework to enable agentic orchestration of paint industry supply chain ecosystems. An agentic architecture orchestrates, in an optimized manner, processes that extend beyond the borders of the organization, with intelligent decision-making enabled through smart sourcing, supplier collaboration, quality and compliance management, and inventory optimization capabilities. While providing specific capabilities and technology requirements for paint manufacturing and retail ecosystems, the research contributes to the wider supply chain domain by defining agentic capabilities as smart decision-making that fulfills user-defined objectives based on constantly changing inputs and constraints
Cloud-Native Big Data Architecture for Real-Time Tax Analytics, Fraud Detection, and Fiscal Governance
Tax Analytics, fraud detection within tax systems, and governance are three areas where data is increasingly processed, thanks to the growing volume of tax records, payment transactions, company trades, and digital services and applications. Real-time processing engines allow continuous ingestion, detection of useful insights while data flows, and preparation of data-ready-to-query in analytical repositories. A novel modular architecture that fits the cloud-native paradigm and employs a mix of batch, micro-batch, and stream processing paradigms allows for a scalable and flexible solution. A monitoring framework allows the detection of problems in the flow and content of the data. The architecture addresses Governance, Compliance, Security, and Auditability aspects without letting these concerns become a bottleneck. The proposed architecture paves the way for a full-fledged implementation, as a clear roadmap emerges. The Cloud-Native principles and the Elasticity, Loose Coupling, Service Orientation, and Microservices properties define how the solution must evolve. Modular and easily interchangeable components are favoured over monolithic architectures, offering flexibility and maintaining the costs for less frequently used modules in the investment background. The reference architecture, its components, the direction of the data flow and the Governance areas of concern are presented, together with the high-level design criteria and relative evaluation metrics
Generative AI for Intelligent Medical Coding and Healthcare Analytics
Generative AI is emerging as a powerful enabler for intelligent and efficient medical coding to facilitate health data analytics. For predictive analytics, generative models are used alongside conventional classification approaches to predict the onset of diseases, while also serving as an internal validation tool for classifier performance. For risk stratification, generative models enhance the unsupervised stratification of patient populations. Adaptive risk-scoring systems are proposed to identify patients likely to require surgery within the next year, with analysis of state transition paths also possible. Considerations related to the privacy and security of health information in using generative models, and for computer-aided healthcare decision and workflow–support systems in general, are presented. Automated model validation frameworks for supporting ICD and CPT coding systems, along with change management in general, are outlined
Generative AI for Autonomous Accounting and Tax Filing: A Cloud-Driven Framework for Smart Financial Services
Generative AI for Autonomous Accounting and Tax Filing: A Cloud-Driven Framework for Smart Financial Services presents an objective, evidence-based analysis of cloud-enabled autonomous financial operations. The general research question is whether autonomous accounting for self-employed individuals using Generative Artificial Intelligence (GAI) and other native AI services can improve the level of automation and value offered in comparison with current software-as-a-service solutions. Other questions pertain to governance, data ingestion, storage, and booking service model design, SLA level determination, and suitability of the overall architecture for autonomous tax filing. Overall, an end-to-end cloud-native, fully automated bookkeeping process is described. Artificial Intelligence (AI) techniques allow the design of smart cloud-based systems able to provide high-level autonomous financial service operations. The architecture covers autonomous accounting for self-employed individuals. Bookkeeping services leverage data generated from connected payment wallets and AI-based services detect anomalies, suggest corrective actions, and generate reports in near real time. Tax computation incorporates Generative AI services to optimize deductions and determine credits eligibility. An outcomes-based governance framework addresses the specific autonomous and Generative AI risk elements present. The approach satisfies the guidelines characterized by the highest level of autonomy in the AI assessment classification. By minimizing manual bookkeeping tasks, the solution delivers greater value compared to current-market cloud solutions.
Explainable Agentic AI Framework for Automated Insurance Fraud Detection and Predictive Risk Intelligence
Automated insurance fraud detection and risk prediction are inherently difficult but critical responsibilities for the insurance service industry. The fraud detection process must rely on explainable artificial intelligence (XAI), especially deep learning methods, to satisfy important requirements for high-stakes decision-making in the domain. An explainable agentic AI framework is therefore proposed to address the challenges and needs in these application areas. Component methods and components of agentic explainable AI are specifically tailored to the task of insurance fraud detection, while a decoupled and independently engineered complementary risk-prediction component develops predictive risk-intelligence measures. An insurance fraud-detection component builds on a convolutional neural network trained on engineered features of insurance fraud—such as insured party, loss date, occupied region, and other features derived from claim descriptions. Automated bidding by fraudsters and the high costs of actuating fraud motivate explainable-response analytics for the detection process. Consequently, XAI methods such as LIME (Locally Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive explanations) are deployed to enhance transparency in multiple detection dimensions and satisfy the explainability requirement. Performance across a range of detection capabilities presents an overall satisfactory solution.
Explainable Agentic AI for Secure and Adaptive Supply Chain Decision Intelligence in Food Service and Financial Systems
Cognition-based theories for agentic, explainable AI inform secure and adaptive methods for food service and financial decision technologies. However, the use of cognition-based theories has largely been limited to studying and modelling human vs. AI agentic decision-making behaviour without a clear application scope or objectives based on practical applications and associated needs. Indeed, no clear strategy or way has yet been proposed or implemented for applying an agentic-based explanation approach in decision intelligence supporting agentic actor repeat operations across diverse application domains. A recently devised framework for AI explanation addressed only a subset of potentially critical domain-specific needs, and it has not yet been tested or applied in a different field, nor any specific area, nor adapted for enhanced security and the inclusion of agentic patterns from cognition. Suitability of forensically annotated, structured deep and predictive decision processes for domain context explanation has not yet been established, nor has an inline explain-by-do-don’t concern-expression capability. Addressing these two related gaps requires examining particular domain food service decision clusters and flows from a specific competence pattern perspective. Food service cognition-based operational support systems with graphical process overlay have become widespread. A database for securing repeat operational actions over other less autonomy-rich decision domains has also recently been established, capturing executive management and board-level financial decision succession and securing decisions from important event signals for high-fidelity decision-intelligence applications within predictive operations and agentic explanation patterns.
Autonomous Insurance Analytics Using Agentic AI for Personalized Coverage and Predictive Risk Intelligence
Autonomous insurance analytics through agentic AI enable personalized premium models, proactive coverage recommendations, risk-averse product feature enhancement, and predictive claims intelligence. Agentic AI exhibits decision-making autonomy, pursues user-centric goals, and interacts with external environments, serving insurance analytics where historical patterns alone remain insufficient. Empirical methods, including data collection, feature engineering, predictive modeling, personalization pipelines, and customer segmentation, operationalize the theoretical foundations. Data ecosystems and ethical considerations encompass sources, quality assessment, bias mitigation, and stakeholder responsibility. System architecture describes components, workflows, and integration within existing analytical platforms. Operating in highly data-driven sectors characterized by large volumes of historical-time series data—spanning product features and pricing, demand-supply interactions, resource and capacity deployment, and claims, loss, fraud, and warranty patterns—traditional analytical methods can produce high-fidelity predictive models. However, such environments can also require proactive actions and decision-making for customers and companies. In insurance underwriting, pricing, and claims domains, developing such proactive actions—one of the principal tenets of agentic AI—constitutes a significant challenge because of the catastrophic and highly risk-averse nature of the sector. The available historical-time-series data for specific products, geographies, and time periods are usually limited by the infrequent occurrence of catastrophic events such as floods and earthquakes. Consequently, merely relying on historical patterns to predict future behavior is rarely adequate for standard insurance companies, especially the ones implementing conservative risk-management strategies or seeking consolidation in the market.
AI-Powered Predictive Analytics for Climate-Aware Flood and Mobile Home Insurance Risk Management
Fulfilling a growing need for climate-aware flood risk management of mobile-home insurance portfolios, this research develops AI-powered predictive analytics that quantify risk and associated capital requirements over time. A comprehensive data analysis prepares a rich database integrating historical claims, exposure characteristics, data records of climate drivers, and additional geospatial, socioeconomic, and business-climate indicators. Using proven methodologies of statistical modeling and machine learning, climate-risk patterns across flood frequency, intensity, and inundation depth are interpreted. Predictive outputs are successively integrated into risk pricing and capital models, stimulating innovations in dynamic risk appetite adaptation, product design, geographical concentration, and operational decision-making. Role players are equipped to monitor evolving tail-risk exposures and arrive at well-grounded, real-time choices. Market forces are pressuring insurers to better serve and support selected vulnerable customer segments, particularly mobile-home communities that routinely live under very high concentrations of climate Risk for flood peril, especially in view of changing climatic trajectories. Climate risk landscapes are being reshaped by factors such as increasing precipitation, longer monsoon seasons, and eventually increasing draughts, all of which differ by region and country. Consequently, demand government and social attention on the part of both regulators and portfolio holders. These growing anxiety are leading role-players to call for comprehensive approaches that enable proactive real-time pricing and service decisions adapted to the fortuitous and/or stress-test-data flow.
Darkshield: Mobile Intrusion Detection using Post-Authentication Failure Analysis and Android Security APIS
Smartphones store sensitive personal, financial, and confidential data, making them frequent targets for theft and unauthorized access. Traditional security methods such as PINs, passwords, and biometrics mainly prevent entry but offer limited protection after repeated intrusion attempts. This paper presents DarkShield, an Android-based intrusion detection and anti-theft system that proactively detects suspicious unlock attempts and alerts the rightful owner. When multiple incorrect PIN or password entries are detected, the system captures the intruder’s image, retrieves the current GPS location, uploads the evidence to cloud storage, and sends alerts through SMS and email. SMS notifications include the location and image link, while emails contain location details and the captured image as an attachment. The system also supports offline SMS alerts, online email notifications, and SIM change detection for unauthorized SIM replacement. To reduce false alerts, a machine learning-based face recognition module verifies whether the captured face matches the authorized user. By integrating evidence capture, location tracking, dual alert mechanisms, SIM monitoring, and intelligent face verification, DarkShield provides an effective solution for smartphone theft detection and unauthorized access prevention.
M. Parvinraj, J. Dinesh, M. Venkatramana, P. Suryaprakash Raj
Intelligent AI-Driven Cloud Frameworks for Cancer Detection and Secure Healthcare Data Transformation
The integration of artificial intelligence (AI) and cloud computing has significantly transformed healthcare systems, particularly in cancer detection and data management. This study proposes an intelligent AI-driven cloud framework designed to enhance early cancer detection while ensuring secure and efficient healthcare data transformation. The framework leverages machine learning and deep learning algorithms for accurate diagnosis using medical imaging and patient data, combined with scalable cloud infrastructure for real-time processing and storage. Security is addressed through advanced encryption techniques, access control mechanisms, and blockchain-based auditing to ensure data integrity and privacy compliance. The proposed system enables seamless data sharing among healthcare providers while maintaining strict confidentiality standards. Furthermore, the framework supports interoperability across different healthcare systems, facilitating collaborative diagnostics and treatment planning. Experimental evaluations demonstrate improved detection accuracy, reduced latency, and enhanced data security compared to traditional systems. This research highlights the potential of AI-powered cloud solutions in revolutionizing cancer diagnostics and secure healthcare data management, paving the way for more efficient, accessible, and reliable medical services.