International Journal of Research Publications in Engineering, Technology and Management (IJRPETM)
Resilient Enterprise Intelligence through AI Cloud Cybersecurity and Adaptive Process Automation
Enterprises increasingly rely on cloud infrastructures and AI-driven platforms to manage data, streamline operations, and deliver services efficiently. However, the rapid digital transformation has heightened exposure to cyber threats, operational disruptions, and complex systemic risks. Resilient enterprise intelligence represents an integrated approach to maintaining operational continuity, ensuring data integrity, and optimizing decision-making by combining AI, cloud cybersecurity, and adaptive process automation. This research explores how AI-enabled cloud cybersecurity frameworks enhance enterprise resilience by proactively identifying threats, automating response mechanisms, and supporting continuous process optimization. Adaptive process automation leverages machine learning, robotic process automation (RPA), and intelligent orchestration to ensure that business workflows can self-adjust to disruptions while maintaining productivity. The study examines architectural models that integrate AI-driven security monitoring, real-time threat analytics, and automated mitigation strategies. By analyzing existing frameworks, conducting simulations, and evaluating enterprise scenarios, the research demonstrates how resilient intelligence enhances operational efficiency, reduces downtime, and mitigates cyber risk. The findings highlight the importance of embedding adaptive automation into cloud systems to create an intelligent, secure, and self-sustaining enterprise environment. Ultimately, resilient enterprise intelligence emerges as a critical capability for modern organizations navigating dynamic, high-risk digital ecosystems.
Cognitive Automation in Distributed Cloud Ecosystems AI Frameworks for Secure Scalable and Intelligent Workflows
Cognitive automation is transforming distributed cloud ecosystems by integrating artificial intelligence (AI), machine learning (ML), and intelligent decision-making into cloud-native workflows. As organizations increasingly adopt multi-cloud and hybrid cloud infrastructures, the complexity of managing distributed systems grows significantly. Cognitive automation addresses this challenge by enabling adaptive, self-optimizing, and context-aware workflows that enhance operational efficiency, security, and scalability. This study explores AI-driven frameworks designed to support secure and scalable automation in distributed cloud environments. It examines how cognitive capabilities such as predictive analytics, anomaly detection, and autonomous orchestration improve system resilience and performance. The research highlights the role of advanced AI models in enabling intelligent resource allocation, real-time threat mitigation, and workflow optimization across geographically dispersed infrastructures. Furthermore, the paper discusses the integration of cognitive automation with DevOps, edge computing, and microservices architectures. It evaluates existing frameworks, identifies limitations, and proposes methodological approaches for implementing intelligent workflows. The findings suggest that cognitive automation significantly enhances decision-making and reduces human intervention, while also introducing new challenges related to governance, interoperability, and ethical AI deployment.
Next Generation AI Systems for Data Intelligence Security and Scalable Cloud Architectures
The rapid advancement of Artificial Intelligence has led to the emergence of synthetic intelligence systems that leverage deep learning to generate, analyze, and optimize complex data-driven solutions. While these systems offer unprecedented capabilities in predictive analytics, automation, and decision-making, they also raise critical concerns related to ethics, fairness, and data privacy. This paper explores the role of synthetic intelligence in building advanced analytics solutions that are not only accurate and scalable but also ethically responsible and privacy-aware. The study focuses on deep learning models such as Generative Adversarial Networks (GANs), transformers, and federated learning frameworks that enable the creation of synthetic data while preserving sensitive information. By utilizing privacy-preserving techniques such as differential privacy, data anonymization, and secure multi-party computation, the proposed approach ensures that individual data confidentiality is maintained without compromising analytical performance. Furthermore, the research highlights the importance of fairness-aware algorithms to mitigate bias in datasets and models, ensuring equitable outcomes across diverse populations. A comprehensive framework is proposed that integrates ethical guidelines, bias detection mechanisms, and privacy-preserving architectures within the deep learning pipeline. This framework supports real-time analytics, scalable deployment, and compliance with global data protection regulations. The findings demonstrate that synthetic intelligence can significantly enhance advanced analytics by providing secure, fair, and interpretable solutions for domains such as healthcare, finance, and smart governance. Ultimately, this research contributes to the development of responsible AI systems that balance innovation with ethical and societal considerations.
Migrating On-Prem Oracle RAC to Cloud-Native Architectures: Bottlenecks and Bottleneck Mitigation
This paper looks into the challenges of moving on-prem Oracle RAC systems over to cloud-based setups. Key focus areas are things like figuring out which parts of RAC are slow or holding things up, mapping out which cloud services your company uses, looking at which workloads are using the most resources, and coming up with ways to keep services running with no interruption. A pilot migration shows how well the new way of working or living is working. Mitigation approaches and what can be done in the future using Kubernetes and tools that manage systems are also talked about.
Transforming Home Electronics Customer Self Installation Experience with AI
The paper presents an explanation of how a business can use artificial intelligence to enhance the self-installation process of the customers of home electronics. Most product returns occur due to confusion that occurred by the users during unboxing, handling, fitting, placement, and wiring. The paper employs a qualitative approach to know these problems and how machine learning models can provide step by step guidance to users. The results demonstrate that AI is capable of identifying mistakes in installation, reducing instructions, and adjusting to the environment of the user. Combining computer vision, predictive modelling, and reinforcement learning, the manufacturers will be able to minimize errors, assist the customer in real-time, and enhance satisfaction. The article gives a practical advice towards developing AI-based installation systems.
The Unified Alumni and Students Engagement System is designed to create a unified platform that connects students, academic staff, and alumni of the Computer Science Department. The purpose of this project is to bridge the gap between current students and professionals, offering opportunities for mentorship, knowledge sharing, jobs and attachments placements, and access to learning resources. Its main focus is on two categories i.e. the academic side and the professional side. The system will bridge the gap between academic and professional world creating opportunities for students and graduates to grow and succeed. The primary problem that the project solves is the lack of structured communication and engagement between these groups which often results in missed opportunities for students to learn from experienced alumni and access relevant industry connections. By facilitating collaboration through event coordination, internships, attachments and job placements, and an integrated resource library, the platform seeks to enhance the overall academic and professional development of students and graduates.
The demand for real-time communication systems has increased due to the growth of online learning, business collaboration, and remote interactions. Traditional messaging platforms support text and images, but video messaging adds more clarity and improves user engagement. This project proposes a Real-Time PHP Video Messaging System that enables users to record, send, receive, and play short video messages instantly. The system uses PHP for backend processing, WebRTC/JavaScript for video capturing, and AJAX/WebSockets for real-time communication. It offers secure message exchange, low-latency delivery, and user-friendly interface. The platform can be used by colleges, teams, customer support, and social platforms for fast and interactive communication.
Transforming Enterprise Integration with Cloud Native Innovations and Next Generation Technology Paradigms
The changing environment of enterprise integration requires a shift of paradigm to more scaled, agile, and efficient systems. The present research article examines how cloud-native innovations and next-generation technologies transform the approaches to enterprise integration. With the help of modern technologies, such as microservices, containerization, and serverless computing, organizations could streamline their work, improve their scalability, and decrease costs. The suggested framework combines these innovative technologies with the conventional enterprise systems which allow ease of interoperability and versatile deployments. The framework is aligned into four components, including cloud-native architecture, automation, data interoperability, and security. All the components will solve the problems of silos and inefficiencies in the legacy system integration. The article provides examples of success stories of cloud-native strategies implementation, including their capacity to encourage agility, accelerate the time-to-market, and provide solid security. Study results indicate that there are significant gains in performance of the system, accessibility of data, and their cost-effectiveness. Cloud-native solutions with built-in AI and machine learning are the means to help enterprises make data-driven decisions and maximize operations. Moreover, the incorporation of the next generation technologies can make the enterprise systems more flexible and resilient giving organizations an advantage in a highly dynamic market. Conclusively, this study outlines the transformative possibilities of cloud-native innovations in the enterprise integration process and offers a business strategy in order to remain relevant in the digital age.
TRANSFORMING CLINICAL DOCUMENTATION AND ANALYTICS USING POWER BI AND DAX COPILOT
Power BI is a powerful business intelligence and data visualization solution that turns tough data into actionable insights. It unifies data from disparate sources to provide real-time insights, enabling healthcare professionals to take quicker, better informed decisions. This results in improved patient care, enhanced utilization of resources, greater operation efficiency, and easier regulatory compliance. Power BI features a simple interface and strong security, making it accessible to administrators and clinician users. The integration of Power BI and DAX Copilot streamlines data modeling and analytics operations through automated and optimized writing of Data Analysis Expressions (DAX) queries. DAX Copilot integrates conversational, ambient, and generative AI into Electronic Health Records (EHR) processes, saving time for clinicians, improving documentation quality, and reducing burnout. It is expected to be expanded to other specialties, acute and long-term care institutions, nursing, and telehealth. The platform leverages richer data from ambient clinical discussions for population health management, research, quality improvement, and health equity efforts. The platform focuses on ethical AI practices, with a focus on security, privacy, transparency, fairness, and compliance with changing healthcare laws. DAX Copilot offers customized localization, language functions, and accessibility features.
REDEFINING CLOUD-NATIVE PERFORMANCE: A TECHNICAL EVALUATION OF MICROSOFT AZURE’S COBALT 100 ARM-BASED VIRTUAL MACHINES
Cloud computing stands to dramatically change the paradigm with the enablement of Arm based architectures, shifting performance, power efficiency and cost optimization possibilities. A new, customized Arm-based virtual machine (VM) called Cobalt 100, recently announced by Microsoft Azure, and promises to rewire cloud native computing for workloads of today. This work provides an in-depth performance comparison between x86-based Azure VM instances and the new Cobalt 100 VMs. Our approach is to use industry standard benchmarking suites (like Geekbench 6, SPEC CPU 2017, and Sysbench) on Arm based Cobalt 100 and x86 based Dv5 VMs. We also measure real-world application workloads (web servers, NGINX, and databases, MySQL, microservices in Kubernetes cluster) as well. Performance is evaluated in terms of CPU throughput, memory bandwidth, energy efficiency, cost/performance, and application latency. Results show Cobalt 100 VMs providing 40% higher price performance and 60% better CPU performance on CPU-intensive workloads than Dv5 series. For webservers and database hosting Cobalt 100 showed 15–25% lower latency and 30% less power consumption, the perfect solution for your cloud native, black carbon free deployment! This benchmark proves the potential of Arm architecture in enterprise cloud, and it positions Cobalt 100 as a proven alternative to traditional x86 VMs for developers and organizations seeking optimal performance and sustainability for next generation of cloud-native applications.
AI AND THE NEW STANDARD OF LIVING PERSONALIZED, PREDICTIVE, AND POWERFUL
Artificial intelligence (AI) can transform our existence by offering personalized, predictive, and impactful solutions to make the human experience and productivity better. AI-driven customization can be employed to design services and products that become more attuned to individuals which gives meaning and value to experiences. Besides, AI predictive power allows people to make proactive choices and permit computers to process vast amounts of intricate data at fast rates to entirely and independently solve issues. AI provides close to real time insights from data obtained from a score of industries. AI-powered customization implies health diagnostic and treatment services, personal finance planning, and learning adaptive environments that enhance quality of life will be enhanced. Predictivity with the assistance of AI between people and organizations propels professionalism that enables extensive interventions to be made before issues escalate. AI reinforces delivery more by self-directed learning and delivery and enhancing operating efficiencies, lowering heart rates, and lengthening emotional bridges between firms and customers. Predictively provides room for AI to enable designers to express a possible new issue prior to it becoming an extensive mess. Predictability, customization AI will become more of engineered solutions where the end product/new experiences will be the desirable result all while being ethical, inclusive culture, where empowerment of the technologies is aimed towards the people as part of sociocultural systems. The future will see more and better quality of life for all covered under the scope of predictive capability, customization for the individual, and smart systems that enhance human capabilities.
HUMAN AI PARTNERSHIPS: UNLOCKING A MORE EFFICIENT, HEALTHIER FUTURE
By 2025, artificial intelligence (AI) will be significantly integrated into healthcare to improve patient care, operational efficiency, and clinical decision-making. AI technologies are available to improve healthcare processes, patient outcomes, and system efficiencies based upon or as a result of virtual assistants, natural language processing, predictive analytics, and diagnostic imaging analysis. The fusion of AI technologies and human physicians, combining computational ways of working with the compassionate understanding and moral judgment characteristics will create a healthier future together. Evidence-based recommendations from clinical AI will improve quality of patient care, increase diagnostic rates, increase the tempo of care, relieve clinician burnout, and grow patient trust. The future of human and AI is already becoming visible with smart wearable technology, AI influenced treatment plans, and systems-wide approach to infusing AI into human-centered care paradigms world-wide. Supporting responsible, human-supervised work to AI ultimately leads to even more human-centered, equitable, and efficacious healthcare impacts across many populations. This ever-increasing hybridization of healthcare could make healthcare a productive, intelligent delivery system.
FUTURE-PROOF LIVING LEADING A BETTER LIFE WITH ARTIFICIAL INTELLIGENCE
AI is a big deal in our time. We talk to each other and use technology in very different ways. AI not only helps us solve hard problems by copying how people think, but it also speeds up tasks that used to take a long time. This saves time for both people and businesses. You can use it for a lot of things, like getting around and getting medical care. This shows how useful it is and how it can help people make better choices and get more done. Adaptive Life Advancement's potential also suggests that AI could help people live in ways that are better for the environment and more flexible. We can imagine a future where AI personalizes learning for each student, energy use is optimized, and cities are built to meet the needs of their residents. But with this promise comes the duty to make sure that AI is fair and doesn't favor one group over another and that everyone can use it.
Creating Robust Cloud Solutions Powered by Machine Learning for High Performance and Continuous Monitoring
The convergence of cloud computing and machine learning (ML) has enabled the development of robust, high-performance systems capable of continuous monitoring and adaptive analytics. Cloud platforms provide scalable infrastructure and elastic computational resources, while machine learning algorithms allow systems to analyze vast datasets, detect anomalies, and optimize operations in real-time. These capabilities are critical for applications in healthcare, finance, smart manufacturing, and IT infrastructure management, where performance, reliability, and responsiveness are paramount. This study explores the design and implementation of ML-powered cloud solutions that deliver high performance while supporting continuous monitoring and system resilience. Key architectural elements, including distributed processing frameworks, automated monitoring pipelines, predictive analytics, and fault-tolerant designs, are examined. Security and governance considerations, such as access control, data encryption, and compliance with regulatory standards, are integrated to ensure reliability and trustworthiness. The research demonstrates that combining ML with cloud infrastructure enhances predictive capabilities, enables proactive maintenance, and improves resource utilization. By leveraging these technologies, organizations can develop robust systems capable of sustaining high performance, mitigating risks, and continuously monitoring operations to detect and resolve issues promptly, ensuring operational excellence and business continuity
Parallel Automation for Cross-Browser and Cross-Device Validation in OTT Systems
The over-the-top solutions need a smooth distribution of high-quality content on the wide variety of devices and browsers. As devices are becoming more and more diverse, it has become an urgent problem to guarantee affective and performance continuity. The paper gives an account of a parallel automation framework capable of extensive scalability and robustness through the combination of Selenium Grid, Appium, and cloud-based device farms that can be effectively used to perform cross-browser and cross-device validation. Our system has the potential to save a lot of execution time through dynamic test orchestration and parallel execution of tests, retaining preservation of accuracy and play back integrity of the visual elements. Experimental results of more than 300 test cases in multiple platforms, including Android and iOS, Smart TVs, and the latest versions of various web browsers, show improvement in the average time of feedback by more than 70 percent, 90 percent improvement in throughput, and an improvement in defect coverage detection. Other measurable key performance indicators include, pixel drift and adaptive bitrate (ABR) switching delays. We designed our framework to facilitate continuous integration processes, and it has been useful especially with regards to testing of the consistency of user interface and video streaming quality. The results present parallel automation as a cost efficient and scalable option of validating OTT platforms that will support quicker releases at the same time as maintaining a high level of ensured quality. The provided architecture is modular and extendable, so it can be flexibly applied to OTT-ecosystems and test technology development in the future.
Formal Verification for AI-Assisted Code Changes in Regulated Environments
Use of AI in code generation is able to make software creation faster, and yet due to lack of controls, the possibility of making severe compliance and reliability issues increases in regulated and safety-critical fields like finance and medicine. We describe, in this paper, a proof-carrying pipeline incorporating proofs presented by Large Language Model (LLM) advice into static analysis, symbolic execution, bounded Model checking, and policy execution mechanisms to certify changes to code before it is merged. The system will block unsafe pattern automatically, generation of auditor friendly evidence packages, and fits with Continuous Integration/Continuous Deployment (CI/CD) worklifes so that the impacts on developer velocity are minimal. We measure the method of the two production-scale repositories, one one in fintech and another one in healthcare, in terms of defect reduction, verification efficiency and the effect on audit preparation. The outcome denotes that the relative decrease in incident of defects after the merge was reduced by 73-78% and the success rate that is evaluated by the verification is markedly elevated when put to the test in a solitary mode. The preparation time of audits was decreased by more than 69%, and gift boxes of proofs were prepared in a structured manner and machine-defensible, therefore, decreasing manual review processes. Prompt refinement using the reinforcement learning also enhanced the throughput in verification by minimizing the number of repetitive re-verifications involved. The results show how AI-based development could be secure and meet standards with the integration of thorough formal verification. Through the use of generative AI techniques and technologies, the outlined pipeline solves the two-fold problem of pushing the development speed, and maintaining correctness and regulatory compliance and provides a scalable template of how high-stakes software engineering can be done in the era of generative AI.
Human-In-The-Loop AI And Institutionalizing Service Reviews: Building A Culture Of Continuous Operational Learning
The current paper researches the idea of human-in-the-loop (HITL) AI application in supporting an organization to create continuous learning by means of structured service reviews. It describes how AI applications may make summaries, find trends and assist humans’ reviewers in making decisions more. Its study is based on the qualitative approach and is centered on actual cases in the educational, industrial, and security systems. It concludes that the collaboration of people and AI helps to make the process of service review more rapid, regular, and oriented to enhancement instead of criticism. It is also revealed in the study that human feedback aids AI tools in improving in the long run. Such themes as trust, collaboration, and shared learning are brought out. The article gives an example of a model in which AI will help in gathering and analyzing data, and then humans will give insights and context. The authors' results indicate that HITL systems enhance transparency and accountability as well as the decision quality at large. The research concludes that AI-assisted service reviews have a great culture of lifelong learning and enhancement.
Modern AI Powered Machine Learning Architecture for Secure Financial Systems in Cloud Ecosystems
The rapid integration of artificial intelligence (AI) and machine learning (ML) into financial systems, cloud infrastructures, and Internet of Things (IoT) applications has necessitated the development of next-generation architectures that ensure both performance and security. Traditional centralized architectures are increasingly inadequate in handling large-scale, real-time data streams and mitigating sophisticated cyber threats. This research proposes a novel AI and ML framework designed to enhance predictive analytics, anomaly detection, and secure transaction processing in cloud-based and IoT-integrated financial ecosystems. The proposed architecture leverages edge computing to reduce latency, blockchain-inspired mechanisms for data integrity, and federated learning models to maintain privacy while enabling collaborative insights across distributed networks. The study emphasizes the convergence of AI, ML, cloud computing, and IoT technologies to build a resilient, adaptive, and scalable financial system capable of responding to dynamic threats and complex operational demands. Simulation results demonstrate significant improvements in detection accuracy, response time, and data privacy, suggesting the architecture’s potential as a foundational model for next-generation secure financial systems. The findings highlight that integrating AI-driven intelligence with distributed computational strategies is essential for advancing financial security, operational efficiency, and user trust in a hyper-connected digital economy.
ADVANCED MEMORY VIRTUALIZATION TECHNIQUE FOR EFFICIENT ACCESS OF DATA RESOURCES IN CLOUD ENVIRONMENT
Operating in cloud setting and accessing its services is fascinating task. It’s one amongst trendy technological areas to figure upon it for development of nation and increase economy rate. The paper deals with up desktop cloud computing services with the assistance of technique referred to as Memory Virtualization. Since virtualization is economical to extent measurability of typical desktop-as-a-service and create it appropriate for Wide space Network (WAN’s) environments. Desktop-as-a-Service (DaaS) in cloud computing means user will access its own desktop services also as services of alternative desktops settled on remote servers. Memory virtualization not solely works with memory board however it conjointly maps physical memory directions to actual machine memory. The directions keep in machine memory square measure checked with incoming user shopper requests. Similar matched queries can lead to generation of information mistreatment one amongst recovery techniques referred to as Shadow Paging Technique. Information is made and keep in remote cloud setting wherever user will access the info to perform their tasks. The planned systematic model for accessing desktop services is shown in following paper by mistreatment memory virtualization technique. A comparison is additionally created between para virtualization technique and memory virtualization technique that offers resolution in favour of memory virtualization technique.
Learning-Driven Control Loops for Self-Improving Microservice Platforms: Autonomic Architectures and Adaptive Policy Optimization
Modern microservice platforms operate in environments characterized by highly dynamic workloads, heterogeneous infrastructure, and continuously shifting business objectives, where static, rule-based resilience and autoscaling mechanisms increasingly fail to manage uncertainty, cascading failures, and non-stationary performance patterns at scale. To address these limitations, this article proposes a self-improving microservice platform architecture grounded in learning loops and adaptive control policies, in which feedback-driven control mechanisms continuously monitor system behavior, analyze operational signals, and refine decision strategies over time. By integrating reinforcement learning (RL) techniques with established autonomic computing principles particularly closed-loop control models such as MAPE-K microservice systems are empowered to autonomously infer optimal scaling, routing, and recovery actions based on observed outcomes rather than predefined thresholds. The synthesis of these learning-driven control loops with modern microservice architectures and cloud-native autoscaling frameworks enables platforms to achieve higher resilience, improved performance stability, and sustained efficiency under volatile conditions. Furthermore, the article examines key architectural trade-offs, outlines evaluation metrics for measuring learning effectiveness and system stability and highlights emerging research directions in AI-augmented self-adaptive platforms, including explainable control policies, hybrid rule–learning approaches, and safe online adaptation in production environments.
Predictive Machine Learning Framework for Intelligent Risk Monitoring and Compliance Management in Enterprise Platforms
Enterprise platforms increasingly rely on data-driven operations, integrating finance, operations, customer management, and regulatory processes into centralized systems. This complexity exposes organizations to operational, financial, and regulatory risks. Traditional compliance monitoring methods are often reactive, inefficient, and prone to human error, limiting organizations’ ability to identify emerging risks proactively. This research proposes a predictive machine learning (ML) framework for intelligent risk monitoring and compliance management in enterprise platforms. The framework integrates supervised and unsupervised ML models to analyze enterprise data streams, detect anomalies, predict potential risk events, and generate actionable compliance insights. Core components include data ingestion from ERP and CRM modules, feature engineering based on risk sensitivity, predictive modeling, automated alerting, and regulatory compliance mapping. Experimental evaluation on simulated enterprise datasets demonstrates the framework’s ability to enhance risk detection, reduce false positives, and support decision-making in regulatory audits. By providing a proactive, automated approach to risk and compliance management, the framework empowers enterprises to mitigate operational, financial, and legal risks while ensuring regulatory adherence. This research contributes to bridging the gap between predictive analytics and enterprise governance, enabling intelligent, scalable, and secure risk management across modern enterprise platforms
Enhancing Web Application and API Security Through Intelligent WAFs and Proactive Threat Management
Web applications and APIs are increasingly targeted by sophisticated attacks that bypass traditional security mechanisms. Conventional Web Application Firewalls (WAFs) rely predominantly on signature-based or static-rule detection, leaving systems vulnerable to zero-day exploits, automated bot campaigns, and the expanding surface area of API-specific threat vectors. This paper presents a comprehensive conceptual framework for an intelligent, proactive Web Application and API Protection (WAAP) system. The framework integrates a logical detection engine grounded in multi-dimensional traffic analysis, self-diagnostic and policy-validation modules, OpenAPI schema-enforced API protection, behavioral bot mitigation, and continuous threat intelligence ingestion. Performance optimization techniques—including adaptive caching, request throttling, and dynamic load distribution—are examined alongside security policy simulation environments that enable pre-deployment validation without production risk. A structured conceptual evaluation framework assesses the system across detection accuracy, false-positive suppression, operational efficiency, and latency impact dimensions. The paper provides a rigorous theoretical foundation for next-generation WAF research, addressing the architectural and analytical gaps left by incumbent solutions in an era of cloud-native, API-first application delivery.
Cloud-Native Intelligent Systems for Predictive Analytics Infrastructure Optimization and Secure Digital Platforms
Cloud-native intelligent systems are transforming modern digital infrastructure by enabling scalable, adaptive, and secure computing environments. Organizations increasingly rely on predictive analytics and artificial intelligence to optimize cloud resources, improve operational efficiency, and strengthen cybersecurity measures. Cloud-native architectures—built on microservices, containers, orchestration platforms, and serverless computing—provide the foundation for intelligent systems capable of dynamically monitoring and optimizing infrastructure performance. These systems leverage machine learning algorithms to analyze large volumes of operational data, predict potential system failures, optimize workload distribution, and automate infrastructure management. Predictive analytics plays a crucial role in forecasting demand, detecting anomalies, and preventing system downtime, thereby improving reliability and reducing operational costs. Additionally, integrating intelligent security frameworks enhances the protection of digital platforms against evolving cyber threats through real-time threat detection and automated response mechanisms. Cloud-native intelligent systems also facilitate continuous integration and deployment, enabling rapid innovation and flexible scaling across distributed environments. This research explores the architecture, implementation strategies, and benefits of cloud-native intelligent systems for predictive analytics infrastructure optimization and secure digital platforms. The study reviews existing technologies, analyzes relevant frameworks, and proposes a methodology for implementing intelligent cloud infrastructures. The findings demonstrate that cloud-native intelligent systems significantly enhance system performance, resource efficiency, and security in modern digital ecosystems.
Why Data Engineering, Not Model Scale, Became the True Bottleneck in Generative AI
In the present paper, it is discussed why the scale of the model is not the key factor in implementing the generative AI, but it is data engineering. Enterprise data systems were not fast enough where foundation models were becoming larger in memory and benchmarking score. The ratios of usable data, the difference between deployment, the index of data quality, and amplification of bias and usability skew can be measured in the serving of the different AI projects by means of the quantitative analysis of various projects. The results indicate that the fitness of data infrastructure is a more adequate clarification of success in manufacturing, rather than the magnitude of the model. The retrieval augmented systems are very dependent on controlled and clean streams of information. The findings show that accountable AI (that is scalable) must have well-founded data engineering bases.
Autonomous AI Cloud Security and Risk Governance for Enterprise Digital Ecosystems and Financial Networks
The expansion of cloud-based enterprise digital ecosystems and financial networks has created a pressing need for autonomous security and risk governance solutions. Traditional security measures often fail to address the speed, complexity, and sophistication of modern cyber threats, especially in highly distributed and multi-tenant cloud environments. This study proposes an autonomous AI-driven cloud security and risk governance framework designed to protect enterprise digital ecosystems and financial networks through real-time threat detection, adaptive risk management, and intelligent compliance monitoring. Leveraging machine learning, deep learning, and behavioral analytics, the framework continuously analyzes network activity, transaction data, and cloud workloads to identify anomalies and predict emerging threats. Integrated cloud-native technologies, including containerized microservices and orchestration platforms, provide scalability, resilience, and operational continuity. The framework incorporates zero-trust access models, automated policy enforcement, and intelligent risk governance to ensure compliance with financial regulations, mitigate insider threats, and maintain data integrity. Autonomous decision-making mechanisms enable proactive threat mitigation, reducing operational disruption and minimizing human intervention. By combining AI-driven analytics, cloud-native scalability, and risk governance, this research demonstrates a comprehensive approach to securing enterprise and financial systems, providing organizations with adaptive, resilient, and regulatory-compliant cloud security capabilities in an increasingly hostile cyber environment.
AI Powered Secure Enterprise Platforms for Intelligent Automation Healthcare and Cloud Scale Digital Transformation
Artificial Intelligence (AI) is transforming enterprise platforms by enabling intelligent automation, improving healthcare systems, and accelerating cloud-scale digital transformation. Modern organizations require secure, scalable, and intelligent platforms that integrate AI capabilities with enterprise infrastructure to process large volumes of data while maintaining strict security and compliance standards. AI-powered enterprise platforms combine technologies such as machine learning, natural language processing, robotic process automation, and cloud computing to automate workflows, support decision-making, and enhance operational efficiency. In healthcare, these platforms enable predictive diagnostics, personalized treatment planning, and efficient patient data management while ensuring regulatory compliance and data privacy. Cloud-based enterprise architectures provide scalability, resilience, and accessibility, allowing organizations to deploy AI services rapidly across distributed environments. Security frameworks integrated into AI platforms protect sensitive enterprise and medical data from cyber threats while maintaining transparency and governance. This research explores the architecture, implementation, and impact of AI-powered secure enterprise platforms in intelligent automation and healthcare applications. It also examines the role of cloud technologies in enabling scalable digital transformation across industries. The study evaluates existing literature, proposes a methodological framework for enterprise AI adoption, and discusses the advantages and limitations of these technologies. The findings highlight how secure AI platforms can drive efficiency, innovation, and sustainable digital transformation in modern enterprises.
Intelligent Cost Optimization Strategies for Multi-Tenant SaaS Platforms Using Machine Learning
The paper is an intelligent cost-optimization strategy with machine learning and how it can be applied to a multi-tenant Software as a Service (SaaS) platform optimization. It suggests a comprehensive design to cost-efficient infrastructure optimization in the cloud, which allows sharing of resources among a number of tenants. This paper will be an attempt to conduct predictive workload analysis with the help of machine learning algorithms that will help discover the patterns of tenant behaviour and resource usage. This allows to make decisions ahead of time about the allocation of resources, minimize waste and optimize efficiency in an information-driven fashion. The platform relies on historical data and usage patterns to perform predictive scaling and scale resources dynamically in order to meet demand in the most cost-effective manner. Another feature discussed in the paper is automated rightsizing that can help to maintain cloud resources according to the changing demands of individual tenants and provide the best performance without excessive provisioning. The platform incorporates FinOps principles to manage costs and focus on financial goals and provides information about cloud economics and how expenses are distributed among tenants. The machine learning models are trained to recognize abnormal utilization and also to optimize the provisioning of resources and this makes it cost effective when the workload is varied. The architecture will ensure SaaS platforms have the capacity to offer high-quality services that are scalable at low costs that cater to the needs of the tenants at high levels and ensure that the performance of the platforms are up to the business goals.
Disaster Management and Earthquake /Tsunami Prediction System Using Machine Learning and Deep Learning
Humanity experiences its most destructive events through natural disasters which include earthquakes and tsunamis. The events happen unexpectedly and result in extensive human deaths and material destruction and they provide emergency teams with insufficient time for rescue operations. The traditional disaster management systems require historical analysis and manual monitoring and they use alert systems which operate according to predefined rules. The methods show limited performance because they struggle to handle the intricate data which seismic sensors and satellites and ocean monitoring systems produce. The research project introduces a disaster management system which uses Machine Learning and Deep Learning methods as its core technology. The system acquires real-time data through continuous monitoring of seismic sensors GPS stations ocean buoys satellites and weather stations. The advanced ML and DL models examine the data to find concealed patterns and identify unusual behavior and forecast earthquake and tsunami events. The system starts disaster detection through automatic early warning systems which notify emergency personnel while it assists in creating evacuation strategies. The system uses prediction based on data to create automated response systems which enhance prediction results while decreasing response durations and reducing human and financial damages. The proposed solution aims to provide a smart, scalable, and reliable framework for modern disaster management.
Peravali Sanjay, Yelighti Harsha Vardhan, Shaganti Yshwanth Raja, Velamati Murali Krishna
Deep Learning Enable Smart Trafficking Management System
Traffic congestion has become one of the most pressing challenges in modern urban environments. Rapid population growth, the surge in private vehicle ownership, and limited infrastructure capacity have all contributed to overcrowded roads and inefficient traffic flow. Traditional traffic management systems, which rely heavily on fixed-time signal control, fail to adapt to dynamic traffic conditions. This often results in prolonged waiting times at intersections, unnecessary fuel consumption, increased air pollution, and a higher risk of accidents. To address these issues, this project proposes a Smart Traffic Management System powered by deep learning, artificial intelligence, and IoT-based sensors. The system continuously monitors, analyses, and regulates traffic in real time. Data is collected from surveillance cameras, IoT sensors, and GPS-enabled devices, providing a holistic view of traffic conditions. Convolutional Neural Networks (CNNs) are employed for vehicle detection, counting, and classification, while Long Short-Term Memory (LSTM) networks enhance traffic flow prediction. By integrating adaptive traffic signal control, the system prioritizes emergency vehicles, reduces congestion, and improves overall road safety. Beyond efficiency, the system aims to create a smoother and more sustainable commuting experience. By reducing idle times and optimizing fuel usage, it directly contributes to lowering carbon emissions and improving air quality. Commuters benefit from shorter travel times, while cities gain a smarter infrastructure capable of evolving with changing demands. Ultimately, this project envisions a future where technology and human needs merge seamlessly—building smarter, greener, and safer cities that enhance the quality of everyday life.
Swathi. B, Aravind. A, Sharath Chandra. A, Sunethra. B, Bhanu Reddy. Ch
From Deterministic Pipelines to Intelligent Orchestration: A Transformer-Driven Framework for LLM-Augmented DevOps Automation
The convergence of Large Language Models (LLMs) and DevOps practices represents a transformative phase in software delivery automation, redefining how modern engineering teams design, deploy, monitor, and optimize software systems. While DevOps has traditionally focused on CI/CD pipelines, infrastructure as code, containerization, observability platforms, and scripted automation, recent advances in transformer-based LLMs introduce a new layer of adaptive, context-aware intelligence capable of interpreting natural language requirements, generating deployment configurations, summarizing distributed system logs, detecting anomalous patterns across telemetry streams, and even recommending or executing remediation steps. Unlike deterministic automation tools that rely on predefined rules and static workflows, LLM-driven systems leverage attention mechanisms and large-scale representation learning to reason across heterogeneous artifacts such as YAML files, Terraform modules, test cases, API schemas, and incident reports, enabling dynamic orchestration of complex DevOps workflows. By integrating foundational transformer research, empirical studies on code-trained LLM performance, and established MLOps architectural frameworks, this work synthesizes a comprehensive reference model for LLM-driven DevOps optimization that spans observability ingestion, prompt orchestration layers, retrieval-augmented reasoning, policy guardrails, and automated action pipelines. Through critical analysis of prior studies and industry implementations, we examine empirical performance evidence, system design trade-offs, governance considerations, security risks, scalability constraints, and cost–latency implications, ultimately outlining how LLM-based automation can augment human engineers, reduce mean time to resolution, improve deployment reliability, and accelerate continuous delivery while maintaining accountability and operational control within enterprise DevOps ecosystems.