International Journal of Research Publications in Engineering, Technology and Management (IJRPETM)
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.
Zero-Trust and AI-Powered Security Architecture for SAP-Centric Enterprise Platforms and Digital Banking Infrastructure
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.
Zero-Trust and AI-Powered Security Framework for Cloud-Native Enterprise Platforms and SAP-Based Digital Ecosystems
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.
Cloud-Based Big Data Processing Architectures for Intelligent Public Transportation Systems
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.
IOT-Based Fire Alarm and Location Tracking System
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.
P. Varsha, P. Krupkar Chary, P. Sathvik, N. Vedasai Varma, S. Rahul
Cloud-Integrated AI Systems for Adaptive Learning Experience Personalization
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.
Cloud-Centric Big Data Pipeline Optimization for Smart Healthcare Monitoring Systems
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.”
Autonomous AI-Powered Cloud-Native Platforms for Continuous Authentication Intrusion Detection Digital Banking Analytics and Secure DevSecOps
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.
AI-Powered Cloud Observability for Proactive Infrastructure Management
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.
Hybrid Cloud Computing: Strategic Integration in the Digital Age with Artificial Intelligence
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.
Real Time Big Data AI Engine for Enterprise Healthcare Risk Prediction with Streaming Analytics Optimization
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.
Unlocking Sales Cloud: A Guide to Smarter Selling in Salesforce
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.
AI-Augmented Infrastructure Governance: Intelligent Risk Detection in Identity-Centric Cloud Platforms
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.
Architecting Real Time Fraud and Risk Detection with AI Enhanced Event Driven Data Pipelines
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 designed to complement rather than replace deterministic controls, combining probabilistic scoring with established business rules. Data engineering considerations played a central role in enabling effective real time fraud detection. Pipelines were required to ingest high velocity event streams reliably, preserve ordering and transactional context, and enrich events with reference data such as customer profiles, device fingerprints, and historical behavior aggregates. Latency constraints imposed strict requirements on pipeline design, limiting the complexity of transformations that could be performed synchronously. As a result, architectures emphasized pre-computed features, incremental aggregation, and efficiency in memory processing to maintain responsiveness under peak load. Operational reliability and governance were equally critical in fraud and risk detection pipelines. Systems handling real time decisions were expected to operate continuously with minimal tolerance for downtime or inconsistent behavior. Enterprises therefore designed pipelines with explicit fault tolerance, backpressure handling, and observability mechanisms to detect degradation before it impacted decision accuracy. Model performance monitoring, data quality checks, and latency tracking were integrated into pipeline operations to ensure that AI driven decisions remained trustworthy and auditable over time. This paper examines real time fraud and risk detection architectures as they were understood and implemented by June 2019, with particular emphasis on AI enhanced data pipelines. It analyzes how event driven ingestion, real time feature computation, and machine learning based scoring were combined to support continuous risk assessment in enterprise environments. The discussion situates these architectures within the technological maturity of mid 2019, highlighting design principles, trade offs, and operational constraints that shaped early real time AI driven fraud detection systems.
These keywords reflect the core architectural and analytical themes relevant to fraud and risk detection systems as of June 2019, emphasizing continuous data ingestion
and the integration of machine learning techniques within operational data pipelines designed for scale