International Journal of Research Publications in Engineering, Technology and Management (IJRPETM)
type: channel
language: en
publication name: International Journal of Research Publications in Engineering, Technology and Management (IJRPETM)
items: Creating Robust Cloud Solutions Powered by Machine Learning for High Performance and Continuous Monitoring
items: Parallel Automation for Cross-Browser and Cross-Device Validation in OTT Systems
items: Formal Verification for AI-Assisted Code Changes in Regulated Environments
items: Human-In-The-Loop AI And Institutionalizing Service Reviews: Building A Culture Of Continuous Operational Learning
items: Modern AI Powered Machine Learning Architecture for Secure Financial Systems in Cloud Ecosystems
items: ADVANCED MEMORY VIRTUALIZATION TECHNIQUE FOR EFFICIENT ACCESS OF DATA RESOURCES IN CLOUD ENVIRONMENT
items: Learning-Driven Control Loops for Self-Improving Microservice Platforms: Autonomic Architectures and Adaptive Policy Optimization
items: Predictive Machine Learning Framework for Intelligent Risk Monitoring and Compliance Management in Enterprise Platforms
items: Enhancing Web Application and API Security Through Intelligent WAFs and Proactive Threat Management
items: Cloud-Native Intelligent Systems for Predictive Analytics Infrastructure Optimization and Secure Digital Platforms
items: Why Data Engineering, Not Model Scale, Became the True Bottleneck in Generative AI
items: Autonomous AI Cloud Security and Risk Governance for Enterprise Digital Ecosystems and Financial Networks
items: AI Powered Secure Enterprise Platforms for Intelligent Automation Healthcare and Cloud Scale Digital Transformation
items: Intelligent Cost Optimization Strategies for Multi-Tenant SaaS Platforms Using Machine Learning
items: Disaster Management and Earthquake /Tsunami Prediction System Using Machine Learning and Deep Learning
items: Deep Learning Enable Smart Trafficking Management System
items: From Deterministic Pipelines to Intelligent Orchestration: A Transformer-Driven Framework for LLM-Augmented DevOps Automation
items: Zero-Trust and AI-Powered Security Architecture for SAP-Centric Enterprise Platforms and Digital Banking Infrastructure
items: Zero-Trust and AI-Powered Security Framework for Cloud-Native Enterprise Platforms and SAP-Based Digital Ecosystems
items: Cloud-Based Big Data Processing Architectures for Intelligent Public Transportation Systems
items: IOT-Based Fire Alarm and Location Tracking System
items: Cloud-Integrated AI Systems for Adaptive Learning Experience Personalization
items: Cloud-Centric Big Data Pipeline Optimization for Smart Healthcare Monitoring Systems
items: Autonomous AI-Powered Cloud-Native Platforms for Continuous Authentication Intrusion Detection Digital Banking Analytics and Secure DevSecOps
items: AI-Powered Cloud Observability for Proactive Infrastructure Management
items: Hybrid Cloud Computing: Strategic Integration in the Digital Age with Artificial Intelligence
items: Real Time Big Data AI Engine for Enterprise Healthcare Risk Prediction with Streaming Analytics Optimization
items: Unlocking Sales Cloud: A Guide to Smarter Selling in Salesforce
items: AI-Augmented Infrastructure Governance: Intelligent Risk Detection in Identity-Centric Cloud Platforms
items: Architecting Real Time Fraud and Risk Detection with AI Enhanced Event Driven Data Pipelines

Creating Robust Cloud Solutions Powered by Machine Learning for High Performance and Continuous Monitoring
type: item
description: 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
subject: Articles
  topic: section
subject: machine learning
  topic: keywords
subject: cloud computing
  topic: keywords
subject: robust cloud solutions
  topic: keywords
subject: high performance
  topic: keywords
subject: continuous monitoring
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: fault tolerance
  topic: keywords
subject: distributed systems
  topic: keywords
subject: real-time monitoring
  topic: keywords
subject: cloud optimization
  topic: keywords
creator: Lakshmi Kalyani Kolli
rights: Copyright (c) 2026
date: 2026-02-11
publication date: 2026-02-11
starting page: 188
ending page: 197
doi: 10.15662/IJRPETM.2026.0901024

Parallel Automation for Cross-Browser and Cross-Device Validation in OTT Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: OTT
  topic: keywords
subject: , Parallel Automation
  topic: keywords
subject: Validation
  topic: keywords
subject: Browser
  topic: keywords
creator: Lingaraj Kothokatta
rights: Copyright (c) 2025
date: 2025-10-10
publication date: 2025-10-10
starting page: 12919
ending page: 12928
doi: 10.15662/IJRPETM.2025.0805030

Formal Verification for AI-Assisted Code Changes in Regulated Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: Verification
  topic: keywords
subject: Code
  topic: keywords
subject: Vulnerability
  topic: keywords
creator: Samanth Gurram
rights: Copyright (c) 2025
date: 2025-12-28
publication date: 2025-12-28
starting page: 1752
ending page: 1761
doi: 10.15662/fr7n5y42

Human-In-The-Loop AI And Institutionalizing Service Reviews: Building A Culture Of Continuous Operational Learning
type: item
description: 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.
subject: Articles
  topic: section
subject: Operations
  topic: keywords
subject: AI
  topic: keywords
subject: Learning
  topic: keywords
subject: Human-in-the-Loop
  topic: keywords
subject: Service Review.
  topic: keywords
creator: Sindhu Gopakumar Nair
rights: Copyright (c) 2025
date: 2025-05-14
publication date: 2025-05-14
starting page: 12137
ending page: 12149
doi: 10.15662/IJRPETM.2025.08030010

Modern AI Powered Machine Learning Architecture for Secure Financial Systems in Cloud Ecosystems
type: item
description: 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.
subject: Articles
  topic: section
subject: Next-generation AI
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Secure Financial Systems
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: IoT Applications
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Data Privacy
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Predictive Analytics
  topic: keywords
creator: R Archana
rights: Copyright (c) 2023
date: 2023-10-10
publication date: 2023-10-10
starting page: 9765
ending page: 9772
doi: 10.15662/IJRPETM.2023.0605018

ADVANCED MEMORY VIRTUALIZATION TECHNIQUE FOR EFFICIENT ACCESS OF DATA RESOURCES IN CLOUD ENVIRONMENT
type: item
description: 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.
subject: Articles
  topic: section
subject: Cloud computing
  topic: keywords
subject: Desktop-as-a-Service (DaaS)
  topic: keywords
subject: virtualization
  topic: keywords
subject: memory virtualization
  topic: keywords
subject: shadow paging
  topic: keywords
creator: SARABU V BALAMURALIDHAR, Dr.V.BALAJI
rights: Copyright (c) 2018
date: 2018-11-10
publication date: 2018-11-10
starting page: 623
ending page: 629
doi: 10.15662/IJRPETM.2018.0103003

Learning-Driven Control Loops for Self-Improving Microservice Platforms: Autonomic Architectures and Adaptive Policy Optimization
type: item
description: 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.
subject: Articles
  topic: section
subject: Self-adaptive systems
  topic: keywords
subject: Microservices
  topic: keywords
subject: Learning loops
  topic: keywords
subject: Autonomic computing
  topic: keywords
subject: MAPE-K
  topic: keywords
subject: Reinforcement learning
  topic: keywords
subject: Control policies
  topic: keywords
subject: Cloud-native systems
  topic: keywords
subject: Self-healing architectures
  topic: keywords
subject: AI-driven resilience
  topic: keywords
creator: Sriram Ghanta
rights: Copyright (c) 2025
date: 2025-01-15
publication date: 2025-01-15
starting page: 11827
ending page: 11835
doi: 10.15662/IJRPETM.2025.0801012

Predictive Machine Learning Framework for Intelligent Risk Monitoring and Compliance Management in Enterprise Platforms
type: item
description: 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
subject: Articles
  topic: section
subject: Predictive Analytics
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Risk Monitoring
  topic: keywords
subject: Compliance Management
  topic: keywords
subject: Enterprise Platforms
  topic: keywords
subject: ERP
  topic: keywords
subject: CRM
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
subject: Automated Risk Assessment
  topic: keywords
creator: Mike P.
rights: Copyright (c) 2026
date: 2026-01-15
publication date: 2026-01-15
starting page: 178
ending page: 187
doi: 10.15662/IJRPETM.2026.0901023

Enhancing Web Application and API Security Through Intelligent WAFs and Proactive Threat Management
type: item
description: 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.
subject: Articles
  topic: section
subject: Web Application Firewall
  topic: keywords
subject: WAAP
  topic: keywords
subject: API Security
  topic: keywords
subject: Bot Mitigation
  topic: keywords
subject: Proactive Threat Management
  topic: keywords
subject: Logical Detection Engine
  topic: keywords
subject: Zero-Day Detection
  topic: keywords
subject: Security Policy Testing
  topic: keywords
subject: Threat Intelligence
  topic: keywords
creator: Ranveer Potel
rights: Copyright (c) 2024
date: 2024-12-12
publication date: 2024-12-12
starting page: 11641
ending page: 11651
doi: 10.15662/IJRPETM.2024.0706024

Cloud-Native Intelligent Systems for Predictive Analytics Infrastructure Optimization and Secure Digital Platforms
type: item
description: 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.
subject: Articles
  topic: section
subject: Cloud-native computing
  topic: keywords
subject: Predictive analytics
  topic: keywords
subject: Intelligent systems
  topic: keywords
subject: Infrastructure optimization
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Microservices architecture
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Digital platforms
  topic: keywords
creator: Poornima G
rights: Copyright (c) 2024
date: 2024-09-16
publication date: 2024-09-16
starting page: 11221
ending page: 11232
doi: 10.15662/IJRPETM.2024.0705012

Why Data Engineering, Not Model Scale, Became the True Bottleneck in Generative AI
type: item
description: 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.
subject: Articles
  topic: section
subject: Generative AI
  topic: keywords
subject: Foundation Models
  topic: keywords
subject: Data Engineering
  topic: keywords
subject: Data Infrastructure Quality
  topic: keywords
subject: Retrieval-Augmented Generation (RAG)
  topic: keywords
creator: Samanth Gurram
rights: Copyright (c) 2023
date: 2023-08-09
publication date: 2023-08-09
starting page: 9028
ending page: 9036
doi: 10.15662/IJRPETM.2023.0604008

Autonomous AI Cloud Security and Risk Governance for Enterprise Digital Ecosystems and Financial Networks
type: item
description: 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.
subject: Articles
  topic: section
subject: Autonomous AI security
  topic: keywords
subject: Cloud risk governance
  topic: keywords
subject: Enterprise digital ecosystems
  topic: keywords
subject: Financial network protection
  topic: keywords
subject: Cloud-native architecture
  topic: keywords
subject: Real-time threat detection
  topic: keywords
subject: Zero-trust security
  topic: keywords
subject: Intelligent compliance
  topic: keywords
subject: Adaptive cybersecurity
  topic: keywords
subject: Cyber resilience
  topic: keywords
creator: Suchitra Ramakrishna
rights: Copyright (c) 2023
date: 2023-12-23
publication date: 2023-12-23
starting page: 9812
ending page: 9820
doi: 10.15662/IJRPETM.2023.0606023

AI Powered Secure Enterprise Platforms for Intelligent Automation Healthcare and Cloud Scale Digital Transformation
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Enterprise Platforms
  topic: keywords
subject: Intelligent Automation
  topic: keywords
subject: Healthcare AI
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Robotic Process Automation
  topic: keywords
subject: Secure Cloud Architecture
  topic: keywords
creator: Sepp Hochreiter
rights: Copyright (c) 2026
date: 2026-03-06
publication date: 2026-03-06
starting page: 549
ending page: 559
doi: 10.15662/IJRPETM.2026.0902010

Intelligent Cost Optimization Strategies for Multi-Tenant SaaS Platforms Using Machine Learning
type: item
description: 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.
subject: Articles
  topic: section
subject: Cost optimization
  topic: keywords
subject: machine learning
  topic: keywords
subject: multi-tenancy
  topic: keywords
subject: SaaS architecture
  topic: keywords
subject: resource allocation
  topic: keywords
subject: predictive scaling
  topic: keywords
subject: FinOps
  topic: keywords
subject: cloud economics
  topic: keywords
subject: workload forecasting
  topic: keywords
subject: automated rightsizing
  topic: keywords
creator: Phanindra Gangina
rights: Copyright (c) 2024
date: 2024-02-01
publication date: 2024-02-01
starting page: 9976
ending page: 9988
doi: 10.15662/IJRPETM.2024.0701010

Disaster Management and Earthquake /Tsunami Prediction System Using Machine Learning and Deep Learning
type: item
description: 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.
subject: Articles
  topic: section
subject: Disaster Management
  topic: keywords
subject: Earthquake Prediction
  topic: keywords
subject: Tsunami Prediction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Early Warning Systems
  topic: keywords
subject: Seismic Data
  topic: keywords
subject: Neural Networks
  topic: keywords
creator: Peravali Sanjay, Yelighti Harsha Vardhan, Shaganti Yshwanth Raja, Velamati Murali Krishna
creator: B.Nirmala
creator: Dr. M. Saravanan
creator: Dr.Prasad Dharnasi
rights: Copyright (c) 2026
date: 2026-03-15
publication date: 2026-03-15
starting page: 540
ending page: 548
doi: 10.15662/IJRPETM.2026.0902009

Deep Learning Enable Smart Trafficking Management System
type: item
description: 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.
subject: Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: Smart Traffic Management System
  topic: keywords
subject: Intelligent Transportation Systems
  topic: keywords
subject: Vehicle Detection
  topic: keywords
subject: Vehicle Classification
  topic: keywords
subject: Traffic Flow Prediction
  topic: keywords
subject: Adaptive Traffic Signal Control
  topic: keywords
subject: Convolutional Neural Networks
  topic: keywords
subject: Long Short-Term Memory
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Smart Cities
  topic: keywords
creator: Swathi. B, Aravind. A, Sharath Chandra. A, Sunethra. B, Bhanu Reddy. Ch
creator: Jitendra. A
creator: Sarvanan. M
rights: Copyright (c) 2026
date: 2026-03-15
publication date: 2026-03-15
starting page: 533
ending page: 539
doi: 10.15662/IJRPETM.2026.0902008

From Deterministic Pipelines to Intelligent Orchestration: A Transformer-Driven Framework for LLM-Augmented DevOps Automation
type: item
description: 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.
subject: Articles
  topic: section
subject: Large Language Models (LLMs)
  topic: keywords
subject: DevOps Automation
  topic: keywords
subject: AIOps
  topic: keywords
subject: MLOps
  topic: keywords
subject: CI/CD Optimization
  topic: keywords
subject: Autonomous Agents
  topic: keywords
subject: Infrastructure as Code (IaC)
  topic: keywords
subject: Code Generation
  topic: keywords
subject: Software Delivery Pipelines
  topic: keywords
subject: Transformer Architecture
  topic: keywords
creator: Shekar Vollem
rights: Copyright (c) 2024
date: 2024-01-09
publication date: 2024-01-09
starting page: 9964
ending page: 9975
doi: 10.15662/IJRPETM.2024.0701009

Zero-Trust and AI-Powered Security Architecture for SAP-Centric Enterprise Platforms and Digital Banking Infrastructure
type: item
description: As enterprise platforms and digital banking infrastructures increasingly migrate to cloud-native and hybrid environments, security challenges have grown in complexity. Traditional perimeter-based security models are no longer sufficient to protect sensitive financial data, SAP-centric enterprise applications, and interconnected digital banking systems. Cyber threats such as account takeovers, insider attacks, ransomware, and fraud demand adaptive, proactive, and intelligent security architectures. This research proposes a Zero-Trust and AI-powered security architecture for SAP-centric enterprise platforms and digital banking infrastructures. The framework integrates identity-centric security, continuous authentication, micro-segmentation, and adaptive access policies with AI-based anomaly detection, threat intelligence, and predictive risk analytics. By enforcing the principle of “never trust, always verify,” the architecture ensures that every access request—internal or external—is authenticated, authorized, and continuously validated. AI modules monitor system behavior, detect suspicious patterns, and respond to security events in real time, enhancing the resilience and integrity of enterprise operations. The proposed solution supports secure integration of SAP modules, cloud services, and banking applications, while maintaining compliance with regulations such as GDPR, PCI DSS, and SOX. The study highlights advantages including proactive threat detection, real-time response, regulatory alignment, and reduced attack surfaces, while also addressing challenges such as implementation complexity, AI model management, and operational overhead in large-scale SAP and financial environments.
subject: Articles
  topic: section
subject: Zero-Trust Security
  topic: keywords
subject: AI-Powered Security
  topic: keywords
subject: SAP Enterprise Platforms
  topic: keywords
subject: Digital Banking
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Predictive Risk Analytics
  topic: keywords
subject: Cloud-Native Security
  topic: keywords
subject: Micro-Segmentation
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
creator: Christian S. Jensen
rights: Copyright (c) 2025
date: 2025-09-24
publication date: 2025-09-24
starting page: 12911
ending page: 12918
doi: 10.15662/IJRPETM.2025.0805029

Zero-Trust and AI-Powered Security Framework for Cloud-Native Enterprise Platforms and SAP-Based Digital Ecosystems
type: item
description: The rapid adoption of cloud-native technologies and enterprise digital ecosystems has significantly transformed how organizations manage business operations and data infrastructure. Enterprise platforms such as SAP are increasingly deployed within cloud environments to support scalable, flexible, and data-driven business processes. However, this transformation introduces new cybersecurity challenges including sophisticated cyberattacks, data breaches, insider threats, and vulnerabilities associated with distributed architectures. Traditional perimeter-based security models are no longer sufficient for protecting modern enterprise environments. As a result, the zero-trust security model combined with artificial intelligence (AI) has emerged as a promising solution for strengthening enterprise cybersecurity frameworks. This research proposes a zero-trust and AI-powered security framework designed for cloud-native enterprise platforms and SAP-based digital ecosystems. The framework focuses on continuous authentication, identity-based access control, real-time threat detection, and automated response mechanisms. AI techniques such as machine learning and behavioral analytics are integrated to detect anomalies, predict potential security threats, and enhance system resilience. Additionally, the framework incorporates secure data integration, encryption protocols, and identity governance mechanisms to protect sensitive enterprise information. The proposed architecture aims to enhance security visibility, reduce attack surfaces, and support proactive cybersecurity strategies in modern enterprise environments. The study highlights the potential benefits and challenges associated with implementing AI-driven zero-trust security frameworks within complex cloud-native and SAP ecosystems.
subject: Articles
  topic: section
subject: Zero-Trust Security
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Cloud-Native Security
  topic: keywords
subject: SAP Security Architecture
  topic: keywords
subject: Enterprise Cybersecurity
  topic: keywords
subject: Threat Detection
  topic: keywords
subject: Identity Management
  topic: keywords
subject: Digital Ecosystems
  topic: keywords
subject: Machine Learning Security
  topic: keywords
creator: Andrea Passerini
rights: Copyright (c) 2025
date: 2025-09-28
publication date: 2025-09-28
starting page: 12900
ending page: 12910
doi: 10.15662/IJRPETM.2025.0805028

Cloud-Based Big Data Processing Architectures for Intelligent Public Transportation Systems
type: item
description: Employment of intelligent public transportation systems is intensifying with the adoption of related concepts such as smart cities, Internet of Things, big data, and artificial intelligence. These approaches require processing of Big Data generated by the growing number of smartphones, digital tickets, transit vehicles, surveillance cameras, and abundance of IoT sensors deployed in operations. Various cloud-based Big Data processing architectural paradigms have emerged. When adopting such paradigms, a public transportation operator must choose the one that best aligns with its needs. A thorough comprehension of the major cloud-based Big Data processing paradigms is vital for transit authorities to enhance data-driven decision-making and accelerate the development of intelligent public transportation. Therefore, a representation of the primary classes of cloud-centric Big Data processing paradigms is presented, along with an analysis of the processing models employed, the elements of Big Data governance management, the requirements for intelligent public transport workloads, and the relevance of cloud-based Big Data processing systems for public transit. The importance of Big Data processing for intelligent management of public transportation is then examined. Subsequently, these considerations are related to key components of the data lifecycle—architecture, data ingestion, and integration. Architectural patterns and integration points are analyzed toward defining a high-level architectural model that fosters seamless interaction among the different components.
subject: Articles
  topic: section
subject: Intelligent Transportation Systems
  topic: keywords
subject: Big Data
  topic: keywords
subject: Cloud Services
  topic: keywords
subject: Data Center
  topic: keywords
subject: GPS
  topic: keywords
creator: Ganesh Pambala
rights: Copyright (c) 2022
date: 2022-12-21
publication date: 2022-12-21
starting page: 7878
ending page: 7892
doi: 10.15662/IJRPETM.2022.0506021

IOT-Based Fire Alarm and Location Tracking System
type: item
description: The Multiple smart infrastructures have reported fire incidents, requiring rapid detection, precise localization, and smart decision support for mitigating risks to lives and property. This paper proposes an advanced fire alarm and fire location tracking system based on smart IoT technology, Artificial Intelligence/Machine Learning (AI/ML) integration and 5G networks. The system detects environmental changes, such as increases in temperature and fluctuations in smoke, and gas concentration, using an extensive network of IoT environmental sensors. Ai/ML algorithms are applied to the model system to predict fires, detect anomalies, and reduce the incidence of false positives before fire events are detected. The system generates localized alerts and transmits real-time environmental data and fire event detection data to cloud servers and emergency responders using high-speed, and low latency, 5G networks. The fire location tracking system uses emergency response unit GPS coordinates and fire location tracking to reduce emergency response time. Experimental evaluations have shown improved detection accuracy, minimum response time, and sustainable real-time communication. The fire location tracking system is an innovative, intelligent, and cost-effective technology to ensure fire safety in smart buildings, industrial domains, and smart cities.
subject: Articles
  topic: section
subject: Internet of Things (IoT)
  topic: keywords
subject: Fire Detection System
  topic: keywords
subject: Smoke Detector
  topic: keywords
subject: Temperature Detector
  topic: keywords
subject: Real-Time Tracking
  topic: keywords
subject: Location Monitoring
  topic: keywords
subject: Emergency Notification System
  topic: keywords
subject: Embedded System
  topic: keywords
subject: Wireless Technology
  topic: keywords
subject: Cloud Monitoring
  topic: keywords
subject: Disaster Management System
  topic: keywords
creator: P. Varsha, P. Krupkar Chary, P. Sathvik, N. Vedasai Varma, S. Rahul
creator: Dr.M.Saravanam, Dr.Prasad Dharnasi
rights: Copyright (c) 2026
date: 2026-03-15
publication date: 2026-03-15
starting page: 560
ending page: 566
doi: 10.15662/IJRPETM.2026.0902007

Cloud-Integrated AI Systems for Adaptive Learning Experience Personalization
type: item
description: In recent years, the proliferation of digital technologies and the advent of big data have created an ideal ecosystem for traditional paradigms in education and associated disciplines to be revolutionized. Such a shift has led to the emergence of new approaches that aim to enhance user experience, make better use of educational resources, and create novel systems for education delivery. Despite these advances, however, gaps still remain, particularly in the design of efficient systems that use data generated by multiple sources, including sensors and learning activities, to create and deliver personalized services. Cloud-integrated artificial intelligence (AI) plays a significant role in learning experience personalization through user modelling. A cloud-integrated AI framework provides tools that enable the development of personalized strategies and algorithms capable of optimizing representation and navigational paths in order to enhance the learning experience. By serving as a hub of user data, the cloud enhances user profiling and supports the deployment of privacy-preserving mechanisms that safeguard information disclosure. The cloud infrastructure also offers education delivery as a service (EDaaS) model, thus lowering the cost of service delivery for both users and universities.
subject: Articles
  topic: section
subject: Adaptive learning
  topic: keywords
subject: personalization
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: cloud computing
  topic: keywords
subject: industrial IoT
  topic: keywords
creator: Ghatoth Mishra
rights: Copyright (c) 2023
date: 2023-12-22
publication date: 2023-12-22
starting page: 9798
ending page: 9811
doi: 10.15662/IJRPETM.2023.0606022

Cloud-Centric Big Data Pipeline Optimization for Smart Healthcare Monitoring Systems
type: item
description: Increased innovation and interaction among connected things may pose a number of administrative challenges. The built system could readily handle important information about medication use by patients in hospitals and their health conditions during treatments. As patients take medications according to prescribed usage, the dispensing keys automatically identify the patients, and the messages could be sent to display on the hospital screens. The feature called a health scan would be used and can assist in helping a patient identify whether any sickness has occurred by just sitting in a particular location. In case a disease is detected in the health scan module and if it is dangerous, then a message can be sent to the patient's primary physician regarding his health condition. The condition verification module would be for verifying the health condition of a patient during the medication time.” A Big Data-Driven Technology in Healthcare helps patients with heart diseases and automatically takes care of these patients by constantly monitoring them on time through the high-speed internet. Smart Healthcare systems equipped with Data Mining Techniques due to which smart patient care is possible. Big data technologies support both the massive storage and the high-performance requirements of data stored in healthcare. Healthcare Data Ubiquitous process consists of data collection, data sharing, data analyzing, results generation, and data visualization. Wireless sensor Network for patient health parameters over the cloud. The patient’s health information can also transfer this data to the hospital through the cloud, the doctor can get the patient health status through the cloud whenever they required. Finger print Technology for storing patient information in the hospital.”
subject: Articles
  topic: section
subject: Cloud-based healthcare analytics
  topic: keywords
subject: Big data pipeline optimization
  topic: keywords
subject: Smart healthcare monitoring
  topic: keywords
subject: Real-time patient data processing
  topic: keywords
subject: Healthcare IoT integration
  topic: keywords
subject: Scalable cloud architecture
  topic: keywords
subject: Distributed data processing
  topic: keywords
subject: Edge-to-cloud computing
  topic: keywords
subject: Predictive health analytics
  topic: keywords
subject: Healthcare data orchestration
  topic: keywords
subject: Machine learning in healthcare
  topic: keywords
subject: Remote patient monitoring systems
  topic: keywords
subject: Secure health data transmission
  topic: keywords
subject: High-performance data pipelines
  topic: keywords
subject: AI-driven healthcare analytics
  topic: keywords
creator: Dileep Valiki
rights: Copyright (c) 2022
date: 2022-12-14
publication date: 2022-12-14
starting page: 7863
ending page: 7877
doi: 10.15662/IJRPETM.2022.0506020

Autonomous AI-Powered Cloud-Native Platforms for Continuous Authentication Intrusion Detection Digital Banking Analytics and Secure DevSecOps
type: item
description: The rapid evolution of digital banking ecosystems demands intelligent, autonomous, and resilient security infrastructures capable of defending against sophisticated cyber threats while ensuring seamless customer experiences. Traditional perimeter-based defenses and static authentication models are inadequate in modern cloud-native environments characterized by distributed microservices, API-driven integrations, and real-time financial transactions. This paper proposes an Autonomous AI-Powered Cloud-Native Platform integrating Continuous Authentication, Intrusion Detection Systems (IDS), Digital Banking Analytics, and Secure DevSecOps automation. The framework leverages machine learning models for behavioral biometrics, anomaly detection, fraud prediction, and adaptive risk scoring. Cloud-native principles such as containerization, orchestration, service mesh security, and API gateways ensure scalable and secure deployment. DevSecOps pipelines embed automated vulnerability scanning, compliance validation, and policy-as-code enforcement into continuous integration and delivery workflows. The proposed architecture enables real-time threat detection, intelligent response orchestration, and privacy-preserving analytics across distributed banking systems. Experimental modeling demonstrates improved fraud detection accuracy, reduced incident response time, and enhanced regulatory compliance. This research contributes a comprehensive autonomous security framework tailored for next-generation digital banking platforms operating in dynamic multi-cloud environments.
subject: Articles
  topic: section
subject: Autonomous AI Security
  topic: keywords
subject: Cloud-Native Architecture
  topic: keywords
subject: Continuous Authentication
  topic: keywords
subject: Intrusion Detection Systems
  topic: keywords
subject: Digital Banking Analytics
  topic: keywords
subject: DevSecOps Automation
  topic: keywords
subject: Behavioral Biometrics
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Secure Microservices
  topic: keywords
subject: API Security
  topic: keywords
subject: Financial Cybersecurity
  topic: keywords
subject: Risk-Based Authentication
  topic: keywords
subject: Compliance Automation
  topic: keywords
creator: Daniele Nardi
rights: Copyright (c) 2026
date: 2026-01-11
publication date: 2026-01-11
starting page: 169
ending page: 177
doi: 10.15662/IJRPETM.2026.0901022

AI-Powered Cloud Observability for Proactive Infrastructure Management
type: item
description: Sustaining cloud-computing infrastructure and services in the face of increasing complexity require comprehensive monitoring and analysis of operating conditions and performance. Observability delivers actionable insights from telemetry data by enabling analysis throughout the entire data life cycle, from data collection and feature engineering through detection, diagnosis, and response. Cloud observability typically focuses on understanding the current state of the infrastructure, but integrating artificial intelligence adds predictive and prescriptive capabilities that inform future resource usage requirements and enable proactive adjustments—behavior that SRE teams are likely to welcome despite initial investment costs. Proactive observability incorporates a range of proactive features, including predicting unusual usage patterns requiring scaling, identifying maintenance opportunities and their risk impact, forecasting demand from new customers or planned marketing campaigns, assessing risk when implementing changes, guiding resource provisioning for new services, and automating runbook execution when known issues occur. These capabilities can be used to augment the quality of service or control costs, with machine learning models learning from past experiences in human-in-the-loop scenarios.
subject: Articles
  topic: section
subject: Cloud
  topic: keywords
subject: Observability
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Infrastructure Management
  topic: keywords
subject: SRE
  topic: keywords
subject: Data Pipeline
  topic: keywords
creator: Shashikala Valiki
rights: Copyright (c) 2023
date: 2023-12-21
publication date: 2023-12-21
starting page: 9784
ending page: 9797
doi: 10.15662/IJRPETM.2023.0606021

Hybrid Cloud Computing: Strategic Integration in the Digital Age with Artificial Intelligence
type: item
description: The convergence of hybrid cloud computing and artificial intelligence (AI) is reshaping the landscape of enterprise IT infrastructure in the digital age. This paper presents a comprehensive analysis of how AI-driven strategies are revolutionizing hybrid cloud architectures, enabling organizations to achieve unprecedented levels of scalability, security, and operational efficiency. Through a structured literature review and quantitative analysis of adoption data across multiple global sectors, this study examines the synergy between machine learning workloads, intelligent auto-scaling mechanisms, and hybrid cloud deployment models. The findings reveal robust growth in AI-integrated cloud adoption across healthcare, finance, education, and government verticals, with large enterprises leading the transition. The paper further compares leading AI-augmented cloud platforms, presents a layered architectural model, and discusses security frameworks tailored for AI workloads in hybrid environments. Strategic implications for IT decision-makers, cloud architects, and researchers are outlined, along with future directions for autonomous cloud management systems.
subject: Articles
  topic: section
subject: Hybrid Cloud
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Auto-scaling
  topic: keywords
subject: Cloud Security
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: SaaS
  topic: keywords
subject: IaaS
  topic: keywords
creator: Aashan Desai
creator: Rugved Gramopadhye
creator: Tina Gada
creator: Jash Shah
rights: Copyright (c) 2026
date: 2026-02-14
publication date: 2026-02-14
starting page: 164
ending page: 168
doi: 10.15662/IJRPETM.2026.0901021

Real Time Big Data AI Engine for Enterprise Healthcare Risk Prediction with Streaming Analytics Optimization
type: item
description: The exponential growth of healthcare data generated from electronic health records (EHRs), medical devices, wearable sensors, insurance claims, and telemedicine platforms has created unprecedented opportunities for predictive risk management. However, traditional batch-processing analytics systems are insufficient for managing high-velocity, high-volume, and heterogeneous healthcare data streams. This paper proposes a Real-Time Big Data AI Engine for Enterprise Healthcare Risk Prediction integrating streaming analytics optimization, scalable cloud infrastructure, and machine learning-based predictive modeling. The framework leverages distributed processing technologies such as Apache Kafka and Apache Spark Streaming for real-time ingestion and computation, alongside advanced AI algorithms including gradient boosting, deep neural networks, and reinforcement learning. The proposed engine supports dynamic risk scoring for clinical deterioration, readmission prediction, fraud detection, epidemic monitoring, and operational optimization. The architecture embeds automated model retraining, drift detection, and explainability modules to ensure transparency, fairness, and regulatory compliance. By combining streaming data pipelines with optimized AI workflows, healthcare enterprises can transition from reactive decision-making to proactive, predictive risk intelligence. The research presents a scalable, low-latency, and secure enterprise-level architecture capable of handling continuous healthcare data streams while maintaining accuracy, reliability, and governance standards.
subject: Articles
  topic: section
subject: Real-Time Analytics
  topic: keywords
subject: Big Data AI
  topic: keywords
subject: Healthcare Risk Prediction
  topic: keywords
subject: Streaming Analytics
  topic: keywords
subject: Apache Kafka
  topic: keywords
subject: Apache Spark
  topic: keywords
subject: Enterprise AI
  topic: keywords
subject: Predictive Modeling
  topic: keywords
subject: Data Drift Detection
  topic: keywords
subject: Cloud Computing
  topic: keywords
creator: Dr Mohit Mathur
rights: Copyright (c) 2026
date: 2026-02-12
publication date: 2026-02-12
starting page: 154
ending page: 163
doi: 10.15662/IJRPETM.2026.0901020

Unlocking Sales Cloud: A Guide to Smarter Selling in Salesforce
type: item
description: In this paper, the author writes about the importance of Salesforce Sales Cloud in the process of making the process of selling in the modern organizations smarter. Since the companies are moving towards the digital and customer centric strategy, Sales Cloud has made available the tools necessary to optimize the customer sales process, data management, teamwork. The qualitative analysis of past researches, case studies, and technological frameworks are used in the study to gain insight into adoption trends of new technology and organizational transformations, and business performance. The results indicate that automation, real-time data, integration, and AI insights enable the sales teams to work more quickly and make them better. Other issues that are brought out in the paper include user readiness and complexity of integration. In general, Sales Cloud is a relevant instrument in facilitating the transformation of sales in the digital domain.
subject: Articles
  topic: section
subject: Salesforce
  topic: keywords
subject: Sales
  topic: keywords
subject: AI
  topic: keywords
subject: Cloud
  topic: keywords
creator: Ranjith Kumar Kollu
rights: Copyright (c) 2025
date: 2025-10-20
publication date: 2025-10-20
starting page: 12891
ending page: 12899
doi: 10.15662/IJRPETM.2025.0805027

AI-Augmented Infrastructure Governance: Intelligent Risk Detection in Identity-Centric Cloud Platforms
type: item
description: The modern-day cloud platforms rely heavily on automated infrastructure and identity systems. Nevertheless, fixed surveillance systems are incapable of identifying complex identity abuse and drifting configuration in dynamic systems. In this paper, an AI-enhanced infrastructure governance system that incorporates supervised learning and anomaly detection as part of identity-centric cloud platforms is outlined. The suggested system will superimpose identity anomaly potential and Configuration Drift Index (CDI) into single Risk Score. The experimental findings are improved significantly compared to the results of the standing monitoring. The precision rose to remain at 0.89 and recall also rose to 0.86, whereas, F1-score rose to 0.87. False positive changed by 0.22 to 0.08. Mean latency of detection was reduced to 74 seconds as compared to 312 seconds. The rate of detection of configuration drift increased to 91% downward of the previous rate of 69%. Significance was found to be at p under 0.01 using statistical testing. The findings show that AI-based governance enhances accuracy in detection, lowers the response time, and enhances adaptive risk management of identity centric cloud environments.
subject: Articles
  topic: section
subject: AI-Augmented Governance
  topic: keywords
subject: Infrastructure-as-Code (IaC)
  topic: keywords
subject: Identity-Centric Cloud Security
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Isolation Forest
  topic: keywords
creator: Nandkumar Niture
rights: Copyright (c) 2025
date: 2025-03-10
publication date: 2025-03-10
starting page: 11802
ending page: 11814
doi: 10.15662/IJRPETM.2025.0802007

Architecting Real Time Fraud and Risk Detection with AI Enhanced Event Driven Data Pipelines
type: item
description: By June 2019, enterprises operating large scale digital platforms faced growing exposure to fraud and financial risk driven by increasing transaction volumes, expanding digital channels, and sophisticated adversarial behavior. Traditional fraud detection approaches, which relied heavily on offline analysis and rule based evaluation applied after transaction completion, were no longer sufficient to protect real time business processes. Delays of even a few minutes in detecting anomalous behavior could result in financial loss, regulatory exposure, and erosion of customer trust. As a result, organizations began prioritizing real time fraud and risk detection capabilities that could operate directly within transaction flows and decision pipelines. This shift toward real time detection required a fundamental rethinking of data pipeline architecture. Fraud and risk systems could no longer depend solely on batch extracted datasets or periodically refreshed analytical models. Instead, they required continuous ingestion of transactional events, contextual enrichment from multiple data sources, and immediate scoring using statistical and machine learning techniques. By mid 2019, event driven data pipelines had emerged as the architectural foundation for meeting these requirements, enabling low latency propagation of transaction data from operational systems into real time decision engines. Artificial intelligence and machine learning increasingly augmented these pipelines by providing adaptive detection capabilities beyond static rule sets. Rather than encoding all fraud logic explicitly, enterprises began deploying models trained on historical transaction patterns to identify subtle correlations, behavioral anomalies, and emerging fraud signatures. These AI enhanced components operated within real time pipelines, scoring transactions as they occurred and contributing to automated or semi automated risk decisions. Importantly, by June 2019, such models were typically designe…
subject: Articles
  topic: section
subject: Real time fraud detection
  topic: keywords
subject: risk analytics
  topic: keywords
subject: AI enhanced data pipelines
  topic: keywords
subject: event driven architecture
  topic: keywords
subject: streaming data processing
  topic: keywords
subject: machine learning models
  topic: keywords
subject: transactional event streams
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: enterprise risk management
  topic: keywords
subject: low latency data integration
  topic: keywords
subject: These keywords reflect the core architectural and analytical themes relevant to fraud and risk detection systems as of June 2019, emphasizing continuous data ingestion
  topic: keywords
subject: real time decision support
  topic: keywords
subject: and the integration of machine learning techniques within operational data pipelines designed for scale
  topic: keywords
subject: reliability
  topic: keywords
subject: regulatory accountability
  topic: keywords
creator: Srujana Parepalli
rights: Copyright (c) 2019
date: 2019-06-04
publication date: 2019-06-04
starting page: 1540
ending page: 1550
doi: 10.15662/IJRPETM.2019.0203003