International Journal of Research Publications in Engineering, Technology and Management (IJRPETM)
AI Driven Self Healing Cloud Architectures for Intelligent Enterprise Reliability Engineering
Artificial Intelligence (AI) driven self-healing cloud architectures are revolutionizing enterprise reliability engineering by enabling automated detection, diagnosis, and recovery from system failures in modern cloud computing environments. As enterprises increasingly depend on distributed cloud infrastructures, ensuring continuous availability, scalability, security, and operational resilience has become a critical challenge. Traditional reliability engineering approaches rely heavily on manual monitoring and reactive maintenance strategies, which are often inadequate for handling dynamic and large-scale cloud ecosystems. AI-driven self-healing architectures address these limitations by integrating machine learning, predictive analytics, automation, and intelligent orchestration mechanisms into cloud operations. This study explores the design, functionality, and significance of AI-driven self-healing cloud architectures in enhancing enterprise reliability engineering. The research examines how AI technologies support predictive failure analysis, anomaly detection, automated remediation, workload optimization, and real-time infrastructure management. It also investigates the role of intelligent orchestration platforms, containerized environments, and cloud-native technologies in building autonomous cloud systems capable of self-recovery. Furthermore, the study discusses implementation challenges including algorithmic complexity, data security, interoperability, and infrastructure costs. The findings indicate that AI-driven self-healing cloud architectures significantly improve operational efficiency, minimize downtime, optimize resource utilization, and enhance business continuity. These architectures are expected to become essential components of future intelligent enterprise systems and resilient digital infrastructures.
DevSecOps: Securing Infrastructure in the Age of Automation
Cloud Native Infrastructure, Infrastructure as Code (IaC), containers, and CI/CD pipelines were used by organizations for better scalability and deployment times. This article explores how DevSecOps can enable security in automated infrastructure environments, and the ways that it integrates security in a continuous manner. DevSecOps adoption was evaluated and its effects on the performance of security using a quantitative research approach. The results indicated that 82% are conducting IaC security scanning, 76% have security part of their CI/CD pipelines and 71% are doing container vulnerability scanning. The research also revealed that vulnerability detection was up to 73% faster, configuration error incident avoidance was up to 67% and deployment reliability increased by up to 72% post adoption of the DevSecOps initiative. The results have revealed that DevSecOps as a key set of principles is to ensure that infrastructure configuration is secure and scalable
Architecting Autonomous Cloud Platforms with AI-Driven Self-Optimization Capabilities
It is with the introduction of sophisticated cloud computing systems that sophisticated operating environments are being built wherein intelligent automation is a must. This paper develops the architectural design of developing smart cloud platforms capable of self-optimizing using artificial intelligence methods. It is proposed that the suggested framework deployed machine learning, real-time telemetry systems, and decision engines with constraints to create smart cloud platforms capable of analyzing usage trends and re-configuring resources on-the-fly. It has a five- layer architecture, which is infrastructure layer, monitoring layer, decision making layer, actuation layer and learning layer. Theoretical study reveals that the framework has the following advantages: it reduces overheads, optimizes the use of resources, improves services, and minimizes the cost of infrastructure. The architecture is cloud native, having its foundation in reinforcement learning and other machine learning and orchestration concepts.
Groundbreaking Data Processing Architectures for Petabyte-Scale Cloud Storage Systems
Data volumes at the petabyte and even exabyte scale are no longer limited to large hyperscale companies. Today, enterprises, research institutions, IoT systems, and AI-driven applications are all producing data at a pace that traditional storage systems were not built to support. Earlier, centralized architectures were sufficient, but with the growing scale, speed, and variety of data, their limitations have become more visible, especially in terms of performance, reliability, and ease of management. This shift has led to a stronger adoption of distributed and cloud- native architectures that are better suited for such environments. This article looks at the key architectural patterns that have evolved in response to these challenges. Instead of focusing only on high-level concepts, it highlights the design aspects that matter in real-world systems, such as how data is partitioned and distributed, how large workloads are handled through parallel processing, how ingestion pipelines remain stable under continuous high data flow, and how cloud-native components like data lakes, object storage, and serverless computing work together as part of a unified system. Based on recent advancements in distributed systems, cloud platforms, and large-scale data engineering, this paper presents a practical framework for building next-generation data architectures. The aim is to provide researchers, architects, and engineers with a clear and grounded reference for designing systems that can maintain performance, reliability, and operational efficiency as data continues to grow.
Hybrid Gen AI Systems: Integrating Small LMs with Large Language Models for Cost-Efficient Enterprise Automation and Decision Intelligence
Integrating small language models with large language models addresses cost and decision-intelligence challenges in enterprise automation. The combination mitigates latency concerns while harnessing the semi-supervised accuracy of LLMs, achieving a lower total cost of ownership in typical Enterprise Generative AI scenarios—model hosting, inference, data transfer, maintenance—by leveraging small-model alternatives. Small language models exhibit high-performance inference capabilities; they efficiently execute simple tasks and process Benchmark data for fine-tuning or evaluation. Although users require low-latency responses, a hybrid setup with LLMs as bad-weather models enhances speed without sacrificing completeness. Exploration of Routing Rules ensures adequate fault containment and multiple Monitoring and Rollback Models enable configuration updates during live execution. Scalable architectures, including dynamic resource scaling, task prioritization, data-caching strategies, and on-demand hardware, improve hybrid deployments. Coupled with cloud economics and energy-efficient edge serving, hybrid Generative – AI systems support a Cost-Effective Green Enterprise strategy. Decision-Intelligence implementations harness Candidate Signals across the Decision Matrix to focus on Explainable Decision Results. Data from various sources is fused into cohesive inputs, with Structured Data augmenting Unstructured Text via Schema-aware Feature Engineering, Context-driven Retrieval-Augmented Generation (RAG), and Semantic-scale Querying. A Robust Data Quality Pipeline validating Input Quality, Provenance, and Query Answering completes the solution. Although enterprise data—text, audio, videos, and images, alone or in combination—is potentially exploitable across the Automation and Decision-Intelligence spectrum, the Adequacy Principle for Utilization requires an integrated Data-Governance Framework that ensures model utility and risk mitigation. GMLIG Questions EMC, ETL Logic, Proprietary Content Protection, Auditing for Model Bias, and Risk Management are key Data-Governance Principles that influence Design.
Efficient Incremental Data Modeling in Apache Iceberg-Based Analytical Pipelines: Partitioning and Snapshot Optimization Strategies
Lakehouse relies on Apache Iceberg to efficiently handle big data analytics in a reliable and scala-able way. But inefficient incremental modeling has the capacity of decreasing the speed of queries and hiking the cost of storage in the long run. This paper gives a quantitative assessment of the partitioning and snapshot retention and compaction policies in terms of Monte Carlo simulations. Findings indicate that scans shrink percentage was increased day to day using partitioning (0.61 to 0.82) and reaction savings were decreased (18.4 seconds to 13.4 seconds). Snapshot expiration policies decreased metadata to data ratio (0.18 to 0.07) and reduced the overall query response (19.3 seconds to 15.8 seconds). Threshold based and daily compaction ensured that average file sizes were above 240 MB and overall efficiency score increased by 0.032 as compared to 0.051. Connected optimization minimized the overall latency by 34 per cent and storage fragmentation by 41 per cent. The results offer viable suggestions in the development of robust and viable Iceberg analytical pipelines.
Autonomous Social Welfare Systems Using Agentic AI for Intelligent Case Management and Resource Allocation
Social welfare programs play a crucial role in supporting vulnerable populations by providing financial assistance, healthcare access, housing support, and other essential services. However, traditional welfare management systems often face challenges such as manual case processing, fragmented data sources, delayed decision-making, and inefficient resource allocation. These limitations can lead to delays in service delivery, increased administrative burden, and reduced effectiveness of welfare programs. Recent advancements in artificial intelligence, particularly Agentic AI, offer new opportunities to modernize social welfare systems. Agentic AI refers to autonomous AI systems capable of reasoning, decision-making, and executing tasks with minimal human intervention. By integrating intelligent agents into social welfare platforms, governments and organizations can automate case management processes, analyze large-scale socio-economic data, and dynamically allocate resources to individuals and communities based on real-time needs. This article explores the architecture and operational framework of autonomous social welfare systems powered by Agentic AI. The proposed approach combines multi-agent systems, machine learning models, and cloud-based data infrastructure to enable intelligent case assessment, eligibility verification, predictive risk analysis, and automated service recommendations. The framework also emphasizes transparency, accountability, and ethical considerations to ensure fairness and privacy protection in automated decision-making. Furthermore, the study discusses potential system components such as data ingestion pipelines, AI-driven decision engines, citizen interaction interfaces, and policy compliance modules. Conceptual diagrams and tables illustrate how agent-based workflows can improve efficiency, reduce operational costs, and enhance service delivery across large-scale public welfare programs. The findings suggest that integrating Agentic AI into social welfare management can significantly improve responsiveness, accuracy, and scalability of welfare services. By enabling autonomous coordination between data systems, policy rules, and service providers, intelligent welfare platforms can support governments in delivering timely assistance while ensuring optimal use of public resources.
Adaptive Risk Based Access Control in Cloud Native Banking Platforms: A Zero Trust Framework for Dynamic Identity Enforcement
Conventional access control systems used in the banking sector are based on unchanging roles and cannot work in the dynamic cloud settings. An Adaptive Risk Based Access Control framework of cloud-native banking platforms based on Zero Trust principles is suggested. The system considers real-time identity signals, like device, behavior, network, and transaction risk to decide on continuous access. Findings indicate that the performance of the system is significantly better than that of RBAC as there is a drop-in false acceptance rate (8.6% to 2.1%), over-privileged access (18.4% to 5.7%) and an increase in detection rate (61.3% to 91.8%). The completion of audits increased to 96.7% and there were enhanced adherence and protection in financial systems
Cognitive Integration Architectures: Unifying AI, Event Streaming, and API Management for Real-Time Enterprise Systems
Modern enterprise ecosystems are undergoing a fundamental transformation driven by the convergence of Artificial Intelligence (AI), event-driven architectures, and advanced API management platforms. As organizations increasingly operate in distributed, hybrid, and multi-cloud environments, traditional integration approaches—based on batch processing, static workflows, and tightly coupled middleware—are becoming insufficient to meet the demands of real-time responsiveness, scalability, and intelligence-driven decision-making This article proposes the concept of Cognitive Integration Architectures (CIA), a unified architectural paradigm that integrates AI capabilities, event streaming infrastructures, and API management layers into a cohesive, adaptive, and self-optimizing enterprise integration fabric. The cognitive layer introduces intelligence into integration workflows by enabling real-time event interpretation, predictive routing, anomaly detection, and autonomous decision orchestration. Event streaming platforms serve as the backbone for high-throughput, low-latency data movement, enabling continuous data flow across microservices, applications, and analytics systems. API management systems provide governance, security enforcement, traffic control, and lifecycle management for enterprise services exposed across internal and external ecosystems The proposed architecture shifts enterprise integration from a deterministic and rule-based model to a context-aware, AI-augmented, and event-native paradigm, capable of dynamically adapting to workload variations, system failures, and evolving business requirements. The article further examines design principles, architectural patterns, and real-world implementation strategies for deploying cognitive integration systems in cloud-native and hybrid enterprise environments Key benefits of this approach include improved operational intelligence, reduced latency in decision pipelines, enhanced scalability, proactive fault detection, and adaptive workload optimization. The paper concludes by highlighting future directions, including generative AI-driven integration orchestration, autonomous middleware systems, and self-healing enterprise integration ecosystems
Generative AI for Smart Agricultural Equipment Automation and Pharmaceutical Crop Yield Prediction
Generative AI shows promise in automating intelligent field equipment, enhancing the efficiency of physical work in agriculture. Current research in agricultural machinery automation, encompassing both field equipment and robotic systems, has witnessed groundbreaking advancements aimed at addressing the increasing labor shortage in agriculture. Additionally, autonomous vehicle research has achieved considerable deployment within government and industrial robotics programs. Farm machinery automation primarily employs symbiotic, classical, task-oriented AI techniques, while robotic harvesting and retrieval remains application-specific. These systems rely heavily on robust and specialized sensors to support perception, state estimation, mapping, planning, and control for real-time operation in unstructured outdoor environments. The second area influenced by GAI is pharmaceutical crop yield prediction. Yield forecasting systems tailored to the pharmaceutical supply chain for cannabis and hemp production have recently emerged. These systems leverage novel generative AI techniques for modelling crop growth and inferring phenotypic characteristics through data fusion involving multispectral and hyperspectral imaging, alongside 2D, 3D, and 4D imaging. A scalable system architecture for GAI implementation in agricultural applications has been presented, addressing aspects related to data pipelines, associated sensor infrastructure, and an edge device network for reducing latency, bandwidth requirements, and privacy concerns.
AI-Based Crop MonitoringPredictive Analytics in Agriculture
Synergy - Integrated Collaborative Code Workspace using Smart AI
Synergy is an AI-powered collaborative coding platform designe 23d to enhance modern software development and learning through an integrated and intelligent environment. Traditional coding tools often lack real-time collaboration, seamless execution, and built-in intelligent assistance, which can hinder productivity and increase development complexity. Synergy addresses these challenges by providing a unified web-based platform that combines live collaboration, automated execution, AI-driven support, and simplified deployment. The platform enables real-time multi-user code editing, allowing multiple developers to work simultaneously on the same project with minimal latency. All changes are instantly synchronized across users, promoting effective teamwork, peer learning, and collaborative problem-solving. An integrated AI assistant enhances the development experience by offering contextual code suggestions, automated debugging, and clear explanations of programming concepts. The AI component leverages intelligent analysis to understand user intent, detect potential errors, and recommend optimizations, thereby reducing development time and improving code quality. This intelligent support helps users identify errors, optimize code, and better understand complex logic, making both development and learning more efficient. The platform is designed with scalability and security in mind, ensuring that multiple users can collaborate without performance degradation while maintaining data integrity and isolation can collaborate without performance degradation while maintaining data integrity and isolation. Furthermore, the platform includes one-click deployment capabilities, streamlining the process of moving applications from development to production. By integrating deployment pipelines within the same environment, Synergy reduces the dependency on external tools and simplifies the overall software development lifecycle. The system also supports project management features such as version tracking, session management, and workspace organization to further enhance collaboration. Overall, Synergy provides a comprehensive, efficient, and user-friendly solution for collaborative coding by integrating real-time interaction, intelligent assistance, automated execution, and simplified deployment within a single platform. It bridges the gap between individual coding environments and collaborative development needs, making it suitable for educational purposes, team-based projects, and professional software engineering workflows.
CyberGuard - An AI-Enabled System for Cyberbullying Detection and Real-Time Cybercrime Alerts
Artificial Intelligence (AI) plays a significant role in analyzing large volumes of unstructured data in cybersecurity applications. This paper presents an AI-driven system for detecting cyberbullying and generating automated cybercrime alerts in real time. The proposed system integrates Machine Learning (ML) and Natural Language Processing (NLP) techniques to classify harmful textual content effectively.Text data is preprocessed using tokenization, normalization, stop-word removal, and transformed into numerical features using TF-IDF. Supervised learning algorithms such as Support Vector Machine (SVM) are employed for accurate text classification. The system also supports multilingual input, enabling detection across diverse user interactions.A browser extension is used to capture user-generated content, which is then analyzed by the detection module. Upon identifying abusive or harmful language, the system triggers alerts to notify users or concerned authorities. Additionally, the system leverages modern language models to enhance contextual understanding and improve detection accuracy.This solution aims to provide a scalable and efficient approach to monitor online interactions and mitigate cyberbullying, contributing to safer digital environments.
Wastewater-Based Circular Economy: Water Reuse in Agriculture and Industry — A Comprehensive Review
Escalating global freshwater demand driven by climate change, population growth, and industrialization necessitates transformative water management strategies. The shift from linear, end-of-pipe wastewater treatment to circular economy (CE) approaches offers significant opportunities for water security, environmental protection, and economic development. This review synthesizes two decades of research on wastewater-based circular economy (WBCE) systems, focusing on water reuse feasibility, nutrient recovery, and resource recovery potential across agricultural and industrial sectors. Globally, wastewater generation reaches 359.4 billion m³ annually, yet only 63% is collected and 52% is treated before discharge (Jones et al., 2021). This wastewater contains substantial recoverable nutrients—16.6 Tg nitrogen, 3.0 Tg phosphorus, and 6.3 Tg potassium annually—which could offset approximately 13–14% of global agricultural fertilizer demand (Qadir et al., 2020). Advanced treatment technologies such as membrane bioreactors (MBRs) and advanced oxidation processes (AOPs) achieve up to 95% BOD removal, 88% nitrogen removal, and 95% phosphorus removal, significantly outperforming conventional systems (Islam, 2025). Economic assessments indicate that integrated circular systems combining nutrient recovery, energy generation, and water reuse can achieve lower life-cycle costs than conventional treatment over 20 years, despite higher upfront capital investment (Barua, 2025). However, large-scale implementation remains constrained by regulatory fragmentation, financial limitations, technological gaps, and social acceptance barriers. Overall, this review identifies key technological innovations, policy frameworks, and institutional reforms required to scale WBCE systems. Integration of advanced treatment technologies with nature-based solutions and resource recovery strategies enables simultaneous progress toward water security, circular economy transition, climate resilience, and broader sustainable development goals.
An IoT-Based Smart Helmet for Real Time Rider Safety Monitoring and Emergency Response System
Two-wheeler accidents contribute significantly to road fatalities due to delayed emergency response and lack of real-time safety monitoring. This paper presents Techmet, an IoT-based smart helmet system designed to enhance rider safety through automated detection and alert mechanisms. The system is divided into four modules: sensing, processing and decision, communication, and control and alert. Sensors such as accelerometer, gyroscope, alcohol sensor, and IR sensor collect real-time data. The processing module analyzes this data to detect accidents, alcohol consumption, and helmet non-compliance. The communication module enables GPS-based location tracking and data transmission, while the control module triggers emergency alerts and manages vehicle ignition. A mobile application provides real-time monitoring and notifications. The system demonstrates improved response time, reliable performance, and a scalable approach to reducing accident-related fatalities.
From Reactive to Proactive: Engineering AI-First Reliability for SAP Mission-Critical Workloads
Within the last twenty years, SAP systems have evolved from mere transactional support tools into comprehensive enterprise resource platforms supporting millions of simultaneous operations per day. Yet, the enterprise software ecosystem continues to treat reliability as an operational afterthought, relying on fundamentally reactive postures even as monolithic architectures have migrated to highly distributed, mission-critical cloud topologies. Contemporary literature champions Artificial Intelligence for IT Operations (AIOps) and Site Reliability Engineering (SRE) as isolated panaceas, engineering predictive models that identify infrastructural decay but deliberately decouple these insights from the execution authority required to halt it. One must ask: does providing human operators with a more sophisticated alerting mechanism actually prevent catastrophic system failures? It fundamentally does not. To bridge the operational chasm between predicting a failure and surviving it, this research proposes a closed-loop, machine-centric continuum that synthesizes predictive analytics specifically addressing dataset imbalance in rare IT incidents with deterministic, automated remediation pipelines. Rigorous empirical stress-testing via intentional chaos engineering within a simulated SAP environment reveals that dynamically tethering the automated intervention threshold to a depleting SRE error budget rapidly neutralizes cascading anomalies. This significantly compresses recovery times while adhering to strict enterprise compliance constraints. Ultimately, by explicitly engineering the human bottleneck out of the immediate critical path, this foundational framework shifts the objective of systems engineering from reactive firefighting to absolute prevention proving that continuous availability is only achievable when intelligent, agentic orchestration is embedded directly into the infrastructure layer.
Low-Code Mobile Application Builder with AI-Assisted Features using Flutter & Firebase
The increasing demand for rapid application development has highlighted the limitations of traditional coding-intensive approaches, particularly in terms of development time, complexity, and accessibility. This project presents the design and implementation of a Low-Code Application Builder, a platform developed to streamline and simplify the process of creating fully functional applications with minimal manual coding. The system provides an intuitive visual interface that enables users to design applications using drag-and-drop components, predefined templates, and configurable elements. It supports key functionalities such as dynamic user interface generation, real-time preview, backend integration, and database connectivity within a unified development environment. This approach allows both technical and non-technical users to efficiently build, modify, and deploy applications.. By incorporating reusable components and automated workflows, the system significantly reduces development effort while maintaining consistency and reliability. In conclusion, the proposed Low-Code Application Builder enhances development efficiency, minimizes technical barriers, and accelerates the application lifecycle, making it a practical solution for modern software development requirements
Harshini R, Dhana Lakshmi E, Gayathri N, Nithesha P
AI Driven Secure Cloud Ecosystems for Federated Intelligence Cyber Resilience and Enterprise Data Governance
AI-driven secure cloud ecosystems are emerging as a foundational paradigm for modern digital enterprises that rely on distributed computing, federated intelligence, and large-scale data governance. As organizations increasingly adopt multi-cloud and hybrid-cloud infrastructures, ensuring cyber resilience and maintaining robust enterprise data governance have become critical challenges. This paper explores an integrated framework where artificial intelligence (AI) is leveraged to enhance security automation, predictive threat intelligence, and adaptive policy enforcement across federated cloud environments. The study focuses on the convergence of federated learning, zero-trust security models, and intelligent orchestration systems to create resilient cloud ecosystems capable of self-adaptation in the presence of evolving cyber threats. It further examines how AI-enabled analytics can improve data governance by ensuring compliance, data lineage tracking, access control optimization, and privacy-preserving computation. Cyber resilience is addressed through continuous monitoring, anomaly detection, and autonomous response mechanisms that minimize downtime and data exposure risks. The research also highlights governance challenges related to cross-border data flows, regulatory compliance, and distributed identity management. By integrating AI with secure cloud architectures, federated intelligence systems can achieve enhanced operational efficiency, reduced security risks, and improved decision-making capabilities. The paper concludes that AI-driven cloud ecosystems represent the next evolutionary step in enterprise cybersecurity and data governance frameworks
Lineage, Traceability, and Reproducibility as Reliability Requirements in Enterprise AI Systems
Artificial intelligence is being applied to key business and compliance choices by more systems in the enterprise. One of the most common systems is concerned with the accuracy of the model and does not factor in the reliability aspect, like the lineage or traceability, or reproducibility. In this paper, we obtain these three aspects as fundamental reliability expectations of enterprise AI. The study was a real enterprise AI applied in 12 months with a before and after quantitative design. Lineage coverage, version control and reproducibility controls were introduced thus, the lineage coverage rose to 0.91 and the success of reproducibility rose to 92% after these tools were applied on structured lineage. Rapid time to incident investigation was less by 66%, audit preparation was also less by 62% and compliance findings were also less by 75%. Monte Carlo simulation also indicated that the risk variability was smaller when the lineage controls had been incorporated. This observation is in full agreement with the results that indicated that integrating lineage, traceability, and reproducibility into AI platforms enhances reliability, audit readiness, and trust in AI results.
Federated Learning Architecture for Privacy Preserving Healthcare Data Governance and Scalable Cloud Native Intelligent Systems
The rapid digitization of healthcare systems has led to an unprecedented growth in sensitive patient data, raising critical concerns regarding privacy, security, and governance. Traditional centralized machine learning approaches require data aggregation, which increases the risk of data breaches and regulatory non-compliance. This paper proposes a federated learning-based architecture designed to enable privacy-preserving healthcare data governance while supporting scalable intelligent systems within cloud-native platforms. The proposed framework allows distributed healthcare institutions to collaboratively train machine learning models without sharing raw data, thereby ensuring data sovereignty and compliance with regulations such as HIPAA and GDPR. Leveraging cloud-native technologies such as containerization, microservices, and orchestration, the system ensures scalability, resilience, and efficient resource utilization. The architecture integrates secure aggregation protocols, differential privacy mechanisms, and blockchain-based audit trails to enhance trust and transparency. Additionally, it supports real-time analytics and decision-making capabilities critical for modern healthcare applications. Experimental analysis demonstrates improved model accuracy, reduced latency, and enhanced data privacy compared to traditional approaches. This research highlights the potential of federated learning combined with cloud-native infrastructure to transform healthcare data governance into a secure, scalable, and intelligent ecosystem.
privacy preservationdistributed machine learning
Building Intelligent Systems from Data Silos Enabling Trustworthy AI and Cloud Ecosystems for Modern Healthcare
Modern healthcare systems generate vast amounts of heterogeneous data across hospitals, laboratories, wearable devices, and electronic health records. However, these data remain fragmented in silos, limiting their potential to support intelligent decision-making. This paper explores the development of intelligent systems that integrate siloed healthcare data to enable trustworthy artificial intelligence (AI) and cloud-driven ecosystems. It highlights the challenges associated with data fragmentation, interoperability, privacy, and security while emphasizing the importance of federated architectures and standardized data exchange protocols. The study proposes a framework that combines cloud computing, AI models, and privacy-preserving techniques such as federated learning and differential privacy to facilitate secure data sharing without compromising patient confidentiality. By leveraging scalable cloud infrastructure, healthcare providers can achieve real-time analytics, predictive modeling, and personalized treatment recommendations. Furthermore, the research underscores the role of trustworthiness in AI systems, including transparency, fairness, accountability, and robustness. The integration of explainable AI mechanisms ensures that clinical decisions are interpretable and reliable. The findings suggest that overcoming data silos can significantly enhance healthcare delivery, improve patient outcomes, and foster innovation. Ultimately, the study advocates for a collaborative, secure, and interoperable healthcare ecosystem powered by trustworthy AI and cloud technologies
A Decentralized Security Model for Preventing Data Breaches in Distributed Environments
Distributed computing environments became an important component of the contemporary business, cloud computing, Internet of Things and data-centric apps. However, they rest on the huge number of interconnected nodes, which can lead to an increased susceptibility to unauthorized access, insider attacks, poor authentication and huge data breaches. The research study proposes a non-centralized method of security against security breaches in distributed environments due to the reduction in the use of a central authority and increase in trust, transparency and resiliency among the networked systems. The proposed model enhances the security in the various levels through the identity verification, distributed access control, encrypted data sharing, node level authentication and real-time anomaly detection. In the model where security choices are decentralized, then the impact of a compromised node and single points of failures becomes lesser. Cryptographic hashing ensures data integrity and traceability, and access permissions are automated using smart contracts. It is also possible to use this framework of continuously monitoring the activity to forecast the potential suspicious behaviour and also transferring the undesired data prior to the breach happening. The article suggests that decentralization of security architecture can offer superior confidentiality, integrity, availability and accountability of the uncommon distributed infrastructures. Overall, the proposed model offers an extensible and adaptable system of protection of sensitive information on the cloud, edge, and enterprise networks. In its own way, it contributes to the body of knowledge regarding cybersecurity in the sense that it provides a practical security model, which can be compatible with the growing need of trustless, transparent, and breach-resilience distributed systems.
A Scalable Microservice Framework for Multi-Modal Logistics Route Optimization
In this paper, I am going to present a scalable logistics route planning microservice software that could be implemented to optimize the supply chain and ensure that it is more efficient, flexible, and resilient. The pressure to identify efficient and sustainable logistic operations in fast, cost-effective and environmentally-safe freight transportation, and the need of faster, agile and multi-modal route planning, implies it. The classic centralised route planning lacks the ability to address the dynamical, large, as well as multi-modal transportation system. We decentralise also the logistics (microservices) such as the data collection, prediction, assigning routes and assigning vehicles, monitoring the traffic conditions, cost and analytics. It offers real time connection to any kind of transport network (road, railway, air and sea) that is scalable and resilient to faults. Both the data sources and the intelligent decision-making models utilise data in real-time; the data sources gather and capture real-time data to optimise dynamically and identify the optimal alternative path to identify the cost-efficient paths. The systems are also capable of interconnecting with other systems like the warehouse management systems, fleet management systems and the enterprise resource planning systems. The needed scale and a range of logistics use-cases are provided with the help of containers and API-based communication and distributed model. In the paper, the benefits of the innovations in route planning, computational issues and sustainable logistics based on microservices are outlined. Plainly, it is by the framework that the logistics companies will have a treasure trove and scalable solution to the logistic and supply chain giving the revitalization to the efficiency and precision as well as decision making capacities of the multi-mode transport.
Smart Artificial Intelligence Framework for Cloud Centric Systems and Continuous Threat Intelligence Evolution
Cloud-centric systems have become the backbone of modern digital infrastructure, enabling scalable, flexible, and cost-efficient computing environments. However, the widespread adoption of cloud technologies has introduced complex cybersecurity challenges, including data breaches, misconfigurations, insider threats, and advanced persistent attacks. This research proposes a smart artificial intelligence (AI) framework designed to enhance security in cloud-centric systems through continuous threat intelligence evolution. The framework integrates machine learning, deep learning, and adaptive analytics to monitor cloud environments, detect anomalies, and predict potential threats in real time. By leveraging dynamic data streams from cloud platforms, the system continuously learns from new attack patterns and updates its threat intelligence models. The proposed approach emphasizes automation, scalability, and proactive defense mechanisms, enabling rapid response to emerging cyber risks. Additionally, the framework incorporates secure data handling, governance, and compliance measures to ensure data integrity and privacy. Experimental evaluation demonstrates improved detection accuracy, reduced response latency, and enhanced adaptability compared to traditional security systems. The study concludes that integrating AI with cloud security infrastructure provides a robust and intelligent solution for safeguarding cloud-based systems against evolving cyber threats.
AI-Enabled Financial Data Governance Using a Secure Cloud-Based Framework for Enterprise Compliance and Automation
The rapid growth of financial data across enterprises has intensified the need for robust governance frameworks that ensure security, compliance, and operational efficiency. This paper proposes an AI-enabled financial data governance model integrated within a secure cloud-based infrastructure to address challenges such as regulatory compliance, data integrity, risk management, and automation. By leveraging artificial intelligence techniques—including machine learning, natural language processing, and anomaly detection—the framework enhances data classification, monitoring, and policy enforcement in real time. Cloud computing provides scalability, flexibility, and cost efficiency, enabling organizations to manage vast volumes of financial data while maintaining stringent security protocols. The proposed framework aligns with global compliance standards and regulatory requirements by automating auditing processes, detecting irregularities, and ensuring transparency in financial reporting. Additionally, it reduces manual intervention, thereby minimizing human error and improving decision-making accuracy. This research highlights how integrating AI with cloud-based governance systems can transform enterprise financial data management into a proactive, intelligent, and adaptive process. The study also evaluates the benefits and limitations of the approach, offering insights into future advancements and adoption challenges. Ultimately, this model supports enterprises in achieving secure, compliant, and efficient financial data governance in an increasingly data-driven environment.
How Servicenow Impacted Accelerating Clinical Trials
The COVID-19 pandemic has highlighted the importance of speed and efficiency in clinical trials, particularly in the life sciences industry. ServiceNow, a digital workflow platform, plays a crucial role in this change by optimizing procedures, boosting communication, and data management. This thesis investigates ServiceNow's impact on clinical trial acceleration, focusing on its features that improve participant engagement, optimize resource allocation, assure regulatory compliance, and harness future technology. ServiceNow automates processes, enabling real-time data access, and centralizes information on participant recruitment, trial methods, and data collection. It also provides analytics for better decision- making and compliance management. Integrating emerging technologies like AI into ServiceNow improves decision-making skills across clinical trial workflows. The thesis emphasizes the necessity of implementing integrated digital solutions to promote operational efficiency, improve patient experiences, and generate improved health outcomes in clinical research.
BUSINESS VALUE OF SERVICENOW FOR HEALTH CARE AND EDUCATION SERVICES
Service Now is a disruptive solution for healthcare and education services, offering efficiency, effectiveness, and better outcomes. As a market leader in digital workflow management, it automates administrative activities, centralizes data management, and enhances collaboration among healthcare practitioners. This allows healthcare workers to focus on high-quality care and ensures accurate patient data. In the education industry, ServiceNow automates complex enrollment, course administration, and student assistance processes, lowering response times and freeing up staff time for student engagement. Its analytics capabilities allow educational institutions to monitor performance indicators, enabling data-driven decisions that improve the learning experience.
Servicenow Create Enterprise Workflows for Various Digitalize Business Processes
In recent years, the digital transformation landscape has evolved at an unparalleled rate, with ServiceNow emerging as a critical platform in changing organizational workflow management. This thesis provides a thorough examination of ServiceNow's Enterprise Workflow capabilities and their revolutionary influence on organizational procedures, efficiency, and digital maturity. This study creates new frameworks for understanding and implementing enterprise-wide digital transformation initiatives by doing thorough research and analysis on implementation strategies, case studies, and empirical data. The study shows how ServiceNow's innovative approach to workflow automation, combined with its robust integration capabilities and intelligent process optimization, allows organizations to achieve unprecedented levels of operational excellence while remaining adaptable in an increasingly dynamic business environment
Organizational Procedures and Digital Maturity
PyHadoopLake: A Python-Native Framework for Building Scalable Lakehouse Architectures on Hadoop
This paper presents PyHadoopLake which is a Python-native framework designed to simplify and modernize the construction of scalable lakehouse architectures on top of the Hadoop distributed file system. The proposed framework integrates extract transform and load operations directly within a Python-driven pipeline while leveraging the distributed processing capabilities of Hadoop. The methodology focuses on combining PySpark with HDFS and Delta Lake table formats to deliver a unified and reproducible data engineering environment. Experimental results demonstrate meaningful improvements in pipeline throughput and data processing latency when compared with traditional Hadoop ETL approaches. The framework offers a practical and extensible solution for organizations seeking to modernize their data infrastructure without abandoning their existing Hadoop investments.
Designing Self Adaptive Cloud and AI Platforms for High Performance Secure and Intelligent Systems
The rapid evolution of cloud computing and artificial intelligence has created the need for platforms that are not only scalable but also capable of adapting dynamically to changing workloads, security threats, and performance requirements. This paper explores the design of self-adaptive cloud and AI platforms that integrate intelligent decision-making mechanisms to optimize system performance, enhance security, and ensure resilience. These platforms leverage machine learning, real-time monitoring, and automated orchestration to continuously analyze system behavior and make autonomous adjustments. Key design principles include elasticity, fault tolerance, predictive analytics, and zero-trust security models. The study highlights how adaptive systems can improve resource utilization, reduce latency, and proactively mitigate cyber threats. Furthermore, it discusses architectural frameworks that combine distributed computing, edge intelligence, and hybrid cloud environments. By incorporating self-learning capabilities, these platforms can evolve over time, responding effectively to emerging challenges and workload variability. The paper concludes by emphasizing the importance of integrating AI-driven automation with cloud infrastructure to build intelligent systems that are efficient, secure, and capable of sustaining high performance in dynamic environments.
BUILDING SECURE MULTI-CLOUD INFRASTRUCTURE FOR MISSION-CRITICAL ENTERPRISE WORKLOADS
The rapid evolution of enterprise IT landscapes has driven organizations toward adopting multi-cloud strategies to enhance scalability, resilience, and operational agility. However, managing mission-critical workloads across heterogeneous cloud environments introduces significant challenges in security, governance, interoperability, and performance consistency. This paper presents a comprehensive, vendor-neutral framework for building secure multi-cloud infrastructure tailored for mission-critical enterprise applications. It explores architectural principles, zero-trust security models, identity federation, data protection mechanisms, and policy-driven automation required to ensure confidentiality, integrity, and availability across cloud platforms. The study further examines cross-cloud networking, workload portability, disaster recovery strategies, and compliance alignment in regulated industries. Emphasis is placed on integrating cloud-native and hybrid security controls, including encryption, micro- segmentation, and continuous monitoring using AI-driven threat detection systems. Additionally, the paper outlines best practices for leveraging Infrastructure as Code (IaC), container orchestration, and DevSecOps pipelines to enforce consistent security postures across providers. Through conceptual models, architectural diagrams, and comparative analysis, this research highlights how enterprises can mitigate risks associated with vendor lock-in, misconfigurations, and fragmented visibility. The proposed approach enables organizations to achieve a secure, resilient, and scalable multi-cloud ecosystem capable of supporting mission-critical workloads while maintaining regulatory compliance and operational excellence.