International Journal of Engineering & Extended Technologies Research (IJEETR)
Welcome to IJEETR The International Journal of Engineering & Extended Technologies Research (IJEETR) is a prestigious, peer-reviewed, open-access international journal committed to fostering innovation, collaboration, and excellence in research across the broad spectrum of engineering and technological sciences. In a rapidly evolving world where technology is reshaping every facet of human life, IJEETR strives to be more than just a journal – it is a knowledge hub where scholars, academicians, researchers, and industry practitioners come together to exchange pioneering ideas, transformative research, and ground breaking solutions to global challenges. We believe in bridging the gap between theory and practice, encouraging interdisciplinary collaboration, and ensuring that innovative discoveries reach a worldwide audience with maximum impact. Our Vision & Mission · Vision: To be recognized globally as a leading platform for the dissemination of high-quality, innovative, and impactful research in engineering and extended technologies. · Mission: o To promote a culture of scientific excellence and innovation. o To provide open access to research, ensuring equitable knowledge sharing. o To accelerate the application of research findings into real-world practices. o To foster collaboration between academia, industry, and society.
Next Generation Healthcare Systems on Cloud Leveraging AI for Adaptive Decision Making and Risk Management
The healthcare sector is experiencing a paradigm shift with the adoption of cloud computing and artificial intelligence (AI) to support adaptive decision-making and effective risk management. This study proposes a next-generation healthcare system architecture that integrates cloud infrastructure with AI-driven analytics to process large-scale clinical data, enhance patient care, and proactively manage healthcare risks. By leveraging cloud scalability, healthcare providers can store and process extensive electronic health records, medical imaging, and real-time patient monitoring data efficiently. AI algorithms, including machine learning and deep learning models, facilitate predictive diagnostics, personalized treatment planning, and early detection of potential medical complications. The proposed system also incorporates automated risk management mechanisms, monitoring operational, clinical, and financial risks to ensure patient safety and regulatory compliance. Preliminary evaluations indicate significant improvements in decision-making speed, predictive accuracy, resource allocation, and risk mitigation compared to conventional healthcare IT systems. The integration of cloud computing and AI not only streamlines healthcare workflows but also supports telemedicine, remote monitoring, and population health management. This research contributes to the development of intelligent, secure, and adaptive healthcare systems, offering a scalable solution for modern medical institutions navigating complex clinical and operational challenges.
Designing Secure and Scalable Microservices for Threat Detection: Engineering Patterns from Endpoint Security Platforms
In this paper, Endpoint Detection and Response (EDR) systems are analyzed to enhance the detecting and responding of a threat using secure and scalable microservices. The study employed quantitative technique on the basis of experimentation, testing, and data examination. Docker and Kubernetes were used to construct the system with microservices that were responsible in data ingestion, analytics, and alerts. Various capacities of microservices and security configurations which included basic TLS, zero-trust, and runtime trust were tested. Throughput and latency, accuracy of detection, and resource utilization were measured in the study. It was revealed that throughput enhanced by over 150 percent in microservices over monolithic systems. The error rate of detection reached 97 percent when there were runtime trust models. Time spent and time taken to recover also enhanced. The conclusion described in the paper is that EDR systems built on microservices have a greater speed, security, and scalability. They are also able to achieve goals of governance, compliance in an improved way compared to traditional set-ups. The study provides research evidence regarding how cloud-native and secure design patterns could be used to create efficient and reliable threat detection sites.
Security-Integrated Test Framework for FedRAMP-Ready Cloud Applications
As the modern SaaS architecture has complex and dynamic nature, it is a challenge to ensure FedRAMP compliance related to the cloud-native environments. The paper entails a framework of a security integrated test automation that can be used to test important FedRAMP controls namely access management, enforcement of encryption, and audit logging accross multicloud environments. The framework takes advantage of the Policy-as-Code principles and reinforcements IaC scanners, such as the tfsec and Regula, the admission controllers in the Kubernetes area, such as the Gatekeeper and behavior monitoring based on SIEM-compatible logs. The CI/CD workflows include tests that can support the continuous security between the code and the runtime. The Terraform and Kubernetes configurations were deployed on the AWS, Azure, and GCP platforms through implementation of policies before and after the deployment on the platforms. Its performance indicates significant increase in the policy detection rates (up to 98 percent) and the speed at which it mitigates (less than 6 minutes) and very low false positive rates. It was also a portable framework, which was demonstrated to work on such DevOps platforms as GitHub Actions, Jenkins, and Azure DevOps. This will automate security checks and checks, and integrate those with current development pipelines, decreasing manual work, problems of compliance drifting as well as aligning the cloud development with the strict FedRAMP requirements. The framework proposed therefore amounts to a feasible, manageable and policy-based ready solution to cloud applications facing governments bridging the disparity between agility in operation and government security regulations.
Architecting Distributed Data Engineering Systems for Real-Time Retail Sales Analytics
Real-time retail analytics addresses immediate business inquiries through instantaneous reporting. Responses to emerging questions can be automated using real-time algorithms, but many others require human intervention, visual exploration, and ad-hoc analysis. These must be performed in true real-time, with freshness of the data being more important than the level of detail. Most commonly, responses take the form of dashboards. Real-time dashboards continuously process data streams and auto-refresh related queries. Compared to historical dashboards that depend on a data warehouse, near-real-time dashboards emerge from a data lake and offer noticeable advantages. Delays often affect time-sensitive dashboards. At the analytical level, the data pipelines may have become blocked due to sudden spikes in traffic; delays may be acceptable in certain tolerable failure modes when data freshness is less crucial than data correctness. Nevertheless, the interactive exploration of data is a near-real-time demand and supportive of decision-makers accountable for a company's results. Late data can even be tolerated in semi-real-time dashboards. Latency continues to be a secondary criterion in the second-highest requirement category: dashboards that answer complex analytical queries with data at a greater logical level of detail.
Training Data Provenance and IP Compliance at Enterprise Scale
Questions about training data lineage pose both major intellectual property (IP), licensing, and regulatory compliance challenges to organizations adopting machine learning (ML) models enter-wide. The paper introduces a reliable, provenance graph based framework to trace assets in initial ingestion to transformation to outputs modeled and support license-aware reasoning and dualmode (static, dynamic) scanning. Conflicts, incompatible licenses and downstream exposures are detected in near real time and automated clearance processes can run. Demonstration using case studies in three areas, multilingual language model training, healthcare Electronic Health Records (EHR) analytics and financial fraud detection proves that the framework can enhance the accuracy of conflict identification leading to an increase to 95% (license review automation) compared to 38 percent (manual). The combined static-dynamic scanning technique detected 99 per cent of latent compliance risks as opposed to 71-78 per cent with the single-mode techniques. Automated clearance not only saved costs of retrofitting 92 percent of the time, it also lower the legal review time by 60 percent. Investigations into performance at ingestion rates as high as 10,000 assets/hour showed processing latencies were less than 350 ms/asset with overhead in the range of <7%, in addition to achieving over 95% accuracy. The findings satisfy that the given resolution operationalizes “trust-by-design” of data and generative outputs, minimizing compliance risk, streamlining legal processes, and growing with ease in high-volume corporate settings. The research itself would bring to the field repeatable and technology-neutral mechanism to integrate compliance into AI life cycle, which links the legal regulation with technical development. This framework would place provenance as a protection against legal liability as well as a vehicle to operational efficiency, allowing organization to comfortably implement their AI system across the granular compliance regulatory setting.
From Manual Email Tracking to AI-Driven Queues Enterprise Case Management Modernization
This article outlines the creation and implementation of a Dynamics 365 Customer Service solution, moving the manual email-based case handling of more than 1,500 Customer Service Representatives to an intelligent SaaS ecosystem. The new system automates the conversion of email to case, queue routing, enforcement of SLAs, along with real-time analytics, greatly reducing the lack of visibility (from 250 hours/month to 0 hours/month) and providing an annual ROI of $105,000. The 8-week pilot with 100 users from multiple sub-groups resulted in a 96% SLA compliance rate and an improvement in the average response time from over 24 hours to 3.2 hours and an increase in the average number of cases completed per agent per day (16.3). Important technical accomplishments include compliance with HIPAA and SOX, integration with CRM and SAP using Power Automate, and creation of other efficient routing rules. Additional plans for this new system include expanding to other channels, adding GenAI auto resolution capability, developing the ability to use data to predict the workforce requirements of the future, establishing customer service as a key asset driven by data and creating reusable reference architecture to facilitate scalable modernizing efforts for Corporations.
A Data-Driven Architecture for Preemptive Cyber Defense Using AI-Based Governance and Autonomous Remediation
Modern cybersecurity programs remain largely reactive despite extensive investment in detection and response technologies. Fragmented tooling, delayed reporting cycles, and limited executive visibility prevent organizations from managing cybersecurity as a predictive and governable system. This paper proposes a data-driven preemptive cyber defense architecture that integrates heterogeneous security telemetry into a unified security data fabric and applies artificial intelligence (AI) for continuous governance scoring, risk forecasting, and autonomous remediation orchestration. The framework introduces formal threat modeling, knowledge-graph–based correlation, reinforcement learning optimization, explainable AI governance, uncertainty modeling, and safety-bounded agentic automation. Experimental evaluation demonstrates improvements in risk visibility, governance efficiency, and decision latency compared with traditional SIEM, SOAR, and GRC approaches. The results suggest a structural shift from reactive cybersecurity operations toward predictive and partially autonomous cyber defense.
Artificial Intelligence Powered Real-Time Data Governance Architecture for Enterprise Lakehouse and Analytics Platforms
Enterprises today generate massive volumes of structured and unstructured data across multiple operational systems, cloud platforms, and IoT networks. Efficient management, governance, and real-time accessibility of this data are critical for business intelligence, compliance, and analytics-driven decision-making. Traditional data governance approaches often struggle to ensure data quality, lineage, security, and compliance in dynamic, high-velocity environments such as lakehouse architectures that combine data lakes and data warehouses. This research proposes an Artificial Intelligence (AI) powered real-time data governance architecture for enterprise lakehouse and analytics platforms. The framework integrates AI and machine learning to automate data classification, metadata management, anomaly detection, quality monitoring, and access control. By continuously analyzing data streams and monitoring governance policies, the system ensures consistent enforcement of data quality, regulatory compliance, and secure access across the enterprise. The methodology involves designing the architecture, implementing AI-based governance modules, and simulating enterprise analytics workloads to evaluate performance, accuracy, and policy adherence. Results demonstrate that AI-powered governance significantly improves data quality, accelerates data-driven decision-making, enhances compliance monitoring, and reduces manual oversight. The proposed architecture supports scalable, real-time, and intelligent management of enterprise lakehouse platforms, enabling organizations to maximize the value of their analytics initiatives while maintaining robust governance standards.
Distributed Data Engineering Pipelines for Real-Time Insurance Claim Processing
In today’s data-driven world, organizations seek to rapidly and accurately convert multi-source data into proactive intelligence to drive smart business decisions. Allowing real-time or near-real-time ingestion, evaluation, and scoring of operational data can improve the speed and efficiency of existing business processes, enabling organizations to uncover risks and opportunities faster than traditional batch-oriented approaches. This paper discusses the ground-up architectural design considerations required to allow real-time and end-to-end processing of multi-source claim data at an insurance firm. The key architecture design principles focus on ingesting and pretreating operational data, stream processing alternatives with integrated data quality and compliance capabilities, and defining and operationalizing real-time scoring and evaluation pipelines with observability and monitoring capabilities. Such a design is instrumental in developing distributed data engineering pipelines that efficiently link cloud-based microservices with inevitable data lakes and warehouses. While the work focuses specifically on claim processing, the principles are applicable to other industry verticals where event-driven implementations of core business processes are required.
AI-Powered Cloud Resource Optimization for Large-Scale Enterprise Applications
Cloud resource consumption is vital to the operational costs of enterprise applications, and it is often a main explanatory variable driving the performance experience. Hyper-scale public cloud providers such as Amazon, Microsoft, and Google have been investing in making resource consumption simple and cost-effective across different resource layers: virtual machines, containers, serverless, and database services. However, the rich functionality, flexibility, and cloud-native advantages of enterprise applications come with complexity that transforms any cost benefits into a burden and can negatively affect resource consumption efficiency, especially at very high scale. Empirical studies have shown that a single large-scale business application deployed and operating in the cloud costs many millions of dollars every month. Addressing these cost issues and improving efficiency at such scale require AI, ML, and Data products at their core. Optimization of resource consumption under cost and performance objectives becomes essential. Solutions using AI for data-driven resource optimization, prediction, and forecasting can be utilized on their own whenever a given cost-resource dimension is analysed or combined into a single cost-performance optimization through a case study on a real-world enterprise cloud application. AI and ML solutions for cloud resource optimization have already been proposed and evaluated in situations such as demand prediction and capacity planning, autoscaling policies, performance-aware workload scheduling, workload-specific service selection, anomaly detection, predictive maintenance, and cost optimization. Promising results have been obtained in terms of cost, performance, and application experience. However, a single solution at a time is not sufficient for producing world-class cost-performance efficiency. A comprehensive AI-powered resource optimization framework leveraging internal or external data sources for the given task is needed to be analysed and deployed with each deployment at every stage of the data-life-cycle process while also considering specific planning needs.
Artificial Intelligence in Cloud Computing
Machine Learning for Resource Allocation
Improving Software Reliability Through Automated Testing Frameworks in Enterprise Systems
Enterprise systems are at the heart of daily operations, and this active presence makes reliability a necessity for optimal efficiency. The paper examines the use of an automated testing structure to increase reliability by transforming checks into routines that can be repeated with the same rules and results that can be reviewed and acted upon in a timely manner. It describes how automation can be integrated with the current delivery, starting with a commit by the developer and continuing to build and test cycles and release readiness, with the feedback loops being very short to identify a mistake before a developer forgets about it. The discussion describes unit, functional, and regression testing in simple terms before demonstrating how the layers can be put together to secure logic, workflow, and long-term support across updates. It also describes the way tool decisions are determined, using the traditional combination of a test-writing framework and a browser-based runner as a real-world example of how the front-end automation is implemented. The case scenario presented in the paper explains how things are going to be different when automation becomes the norm. The concluding sections summarize the important quality steps based on which teams check the progress and the test suite health when systems increase in size.
AI Enabled Digital Twin and IoT Integrated Architecture for Smart Industry Automation and Predictive Maintenance
The emergence of Industry 4.0 has driven the integration of advanced technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and Digital Twin (DT) systems to enhance industrial automation, operational efficiency, and predictive maintenance. Manufacturing and industrial enterprises face increasing challenges in managing complex machinery, monitoring system health, and optimizing production processes while minimizing downtime. AI-enabled Digital Twin and IoT-integrated architectures offer a promising solution by providing real-time monitoring, simulation, and predictive analytics capabilities. This research proposes a comprehensive AI-enabled Digital Twin and IoT-integrated framework for smart industry automation and predictive maintenance. The framework combines sensor-based IoT data collection, real-time modeling through digital twins, and AI-driven predictive analytics to anticipate equipment failures, optimize machine performance, and support decision-making in industrial environments. The proposed system enables continuous monitoring of industrial assets, dynamic simulation of operational scenarios, and automated maintenance scheduling based on predictive insights. The research methodology employs system architecture modeling, case-study simulations, and comparative analysis with conventional maintenance systems to validate the framework’s effectiveness. Findings indicate that the proposed AI-enabled digital twin architecture can significantly reduce equipment downtime, enhance process efficiency, optimize resource utilization, and support proactive maintenance strategies, contributing to improved industrial productivity and resilience in smart manufacturing ecosystems.
Deep Learning Driven Predictive Threat Detection Framework for Secure Financial and Healthcare Cloud Platforms
Cloud computing has become a fundamental infrastructure for financial institutions and healthcare organizations due to its scalability, cost efficiency, and accessibility. However, the migration of sensitive financial transactions and medical records to cloud environments introduces significant cybersecurity risks, including data breaches, ransomware attacks, insider threats, and advanced persistent threats. Traditional rule-based security mechanisms are often insufficient to detect sophisticated and evolving cyberattacks in real time. This research proposes a deep learning driven predictive threat detection framework designed to enhance the security of cloud platforms used in financial and healthcare systems. The proposed framework integrates deep neural networks, anomaly detection models, and behavioral analytics to analyze large-scale cloud activity logs and network traffic patterns. By leveraging deep learning algorithms such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks, the framework can identify abnormal behaviors and predict potential threats before they cause damage. The model continuously learns from historical and real-time data to improve detection accuracy and reduce false positives. The study evaluates the framework’s performance using benchmark cybersecurity datasets and cloud environment simulations. The results demonstrate that deep learning-based predictive security systems significantly enhance proactive threat detection, improve incident response time, and strengthen data protection for sensitive financial and healthcare information in cloud infrastructures.
Predictive Vulnerability Intelligence for Autonomous Infrastructure Security and Compliance Management with AI-Enabled Analytics
The rapid adoption of cloud computing and digital transformation has created increasingly complex enterprise infrastructures, exposing organizations to sophisticated cyber threats and regulatory compliance challenges. Traditional security models are reactive, often detecting vulnerabilities after exploitation, which can lead to data breaches, operational downtime, and regulatory penalties. To address these challenges, predictive security approaches leveraging artificial intelligence (AI) are emerging as critical tools for proactive vulnerability management. This research proposes an AI-enabled predictive vulnerability intelligence framework designed for autonomous cloud infrastructure security and compliance management. The framework leverages machine learning models to analyze system logs, network traffic, configuration data, and threat intelligence feeds, enabling real-time identification of vulnerabilities and potential attack vectors. By integrating predictive analytics with cloud-native automation, the system can automatically prioritize risks, recommend mitigations, and enforce security policies across multi-cloud and hybrid environments. In addition, the framework incorporates compliance management modules that ensure adherence to regulatory standards such as HIPAA, GDPR, PCI DSS, and SOC 2. Autonomous decision-making and self-healing mechanisms minimize human intervention, reduce response times, and enhance operational resilience. The proposed architecture enables enterprises to proactively secure cloud infrastructures, maintain compliance, and optimize resource allocation, ensuring a scalable, intelligent, and resilient digital ecosystem.
AI enabled predictive vulnerability intelligence
autonomous cloud infrastructure security
Next Generation Enterprise Architecture for SAP Cloud Systems Leveraging AI Driven Analytics and Hybrid Infrastructure
The rapid evolution of cloud computing, artificial intelligence, and enterprise analytics has reshaped how organizations deploy, manage, and scale enterprise systems such as SAP. Modern enterprises require architectures that not only provide operational efficiency but also enable intelligent decision-making, data-driven insights, and agile digital transformation. This research explores the design and implementation of a next-generation AI-driven enterprise architecture for SAP cloud systems, integrating intelligent analytics, hybrid cloud infrastructure, and digital transformation strategies. The proposed framework leverages machine learning algorithms, real-time analytics, and hybrid infrastructure orchestration to optimize resource utilization, automate decision-making processes, and ensure robust system performance. By integrating AI into SAP cloud platforms, enterprises can achieve predictive insights, improved operational efficiency, and enhanced scalability across multi-cloud and on-premise environments. The research methodology combines architectural modeling, simulation experiments, and performance evaluation using enterprise datasets and SAP workloads. The results indicate that AI-driven architectures significantly improve system adaptability, predictive analytics accuracy, and hybrid infrastructure management, enabling organizations to accelerate digital transformation initiatives while maintaining high levels of security, resilience, and operational efficiency. The study provides a scalable, intelligent, and flexible enterprise architecture framework that addresses the challenges of modern SAP deployments and supports long-term organizational innovation.
Next Generation AI Driven Security and Analytics Framework for Cloud Native Enterprise Systems Financial Platforms and IoT Infrastructure
The proliferation of cloud-native enterprise systems, financial platforms, and IoT infrastructure has revolutionized digital operations but simultaneously exposed organizations to increasingly sophisticated cyber threats. Traditional security measures are often insufficient to detect, predict, and mitigate dynamic attacks across distributed environments. This study proposes a next-generation AI-driven security and analytics framework designed to enhance resilience, real-time threat detection, and intelligent decision-making for modern enterprise ecosystems. Leveraging advanced machine learning, deep learning, and behavioral analytics, the framework continuously monitors network traffic, transaction data, and IoT device activity to detect anomalies, predict cyber risks, and automate response mechanisms. Cloud-native technologies, including microservices, containerization, and orchestration platforms, ensure scalability, high availability, and operational flexibility. The framework also incorporates zero-trust security principles, identity and access management, and intelligent data governance to maintain regulatory compliance and protect sensitive information. By integrating predictive analytics with AI-driven security operations, the proposed architecture enables proactive threat mitigation, minimizes operational disruption, and enhances enterprise resilience. This research highlights the role of AI-enhanced security analytics in securing distributed and heterogeneous infrastructures, offering a holistic framework capable of addressing the evolving cyber threat landscape across cloud-native financial platforms and IoT-enabled enterprise systems.
AI Enabled Secure Digital Ecosystem for Cloud Computing Enterprise Automation and Cyber Physical Systems
Artificial Intelligence (AI) has become a transformative technology in modern digital infrastructures, enabling intelligent automation, enhanced security, and scalable computing environments. The development of an AI-enabled secure digital ecosystem plays a critical role in integrating cloud computing, enterprise automation, and cyber-physical systems (CPS). Such ecosystems allow organizations to process massive data streams, automate complex processes, and ensure secure communication between digital and physical components. Cloud computing platforms provide the scalable infrastructure required for deploying AI algorithms, while enterprise automation systems streamline organizational operations and improve efficiency. At the same time, cyber-physical systems integrate computational intelligence with physical devices such as sensors, industrial machines, and smart infrastructure to enable real-time monitoring and control. However, the rapid adoption of AI-driven digital ecosystems introduces significant challenges related to cybersecurity, data privacy, system interoperability, and governance. Secure digital architectures must incorporate advanced protection mechanisms including encryption, intelligent threat detection, and identity management frameworks to protect critical enterprise and industrial data. This research explores the architecture, technologies, and implementation strategies of AI-enabled secure digital ecosystems. The study also evaluates their role in enhancing cloud computing services, enabling enterprise automation, and supporting cyber-physical systems. Finally, the research highlights the advantages, limitations, and future directions of secure AI ecosystems in driving sustainable digital transformation across industries.
One Stop Personalized Career and Educational Advisor
The One-Stop Personalized Career and Educational Advisor is an intelligent guidance system designed to help individuals make informed decisions about their career paths and educational journeys. The platform analyzes a user’s interests, skills, academic background, and career goals to provide personalized recommendations on suitable careers, courses, certifications, and learning pathways. By integrating career counseling and educational planning into a single solution, the system reduces confusion and helps users clearly understand the options available to them. This advisor acts as a comprehensive support tool for students and job seekers by offering career roadmaps, skill-gap analysis, and recommendations aligned with current industry trends. It empowers users to plan short-term and long-term goals efficiently while saving time and effort spent searching across multiple platforms. Ultimately, the system aims to bridge the gap between education and employment by delivering accurate, customized, and practical guidance in one centralized platform. the One-Stop Personalized Career and Educational Advisor provides an effective and user-centric solution for career and academic planning. By delivering customized guidance based on individual profiles and industry requirements, the system helps users make confident and well-informed decisions. This integrated approach not only simplifies career exploration but also enhances employability and lifelong learning, making it a valuable tool for students and job seekers in today’s dynamic professional environment.
Nithin. A, Harish. B, Prashanth. B, Shirisha. CH, Raviteja. CH
Handwritten Character Recognition Using Neural Networks
In the c formation and automation, efficient ai.d accurate text recognition plays a crucial role in bridging the gap between human writing and comp riting and compu, interpretation. Our project, the Handwritten Character Recognition (HCR) System using leural Networks, addresses this need by establishing an intelligent model capable of identifying and classifying handwi racter with high precision. The system utilizes deep learning techniques, specifically Convolutional Neurai Networks (CNNs), to analyze handwritten input images and cotvert them into machine-readable text. Built on a scalab t neural network architecture, this system employs advanced prepro- cessing methods su nage normalization, noise reduction, and segmentation to ensure accurate recognition. The crained using a large set of handwritien samples, enabling the model to learn diverse writing styles and patterns. The The trained model then predicts the corresponding characters with improved accuracy through multiple feature extre tion and classification layers.
K.Prasad, K.Rakesh, G.Vishnu, G.Raju, K.Vardhan
Deepfake Face Detection Using Machine Learning
Generative artificial intelligence has evolved rapidly, especially nowadays, allowing to create realistic images with the help of GANS and Diffusion models, which can be used creatively but are a serious threat to forensic integrity and information security, and are often used to commit identity fraud and false information. This research describes full stack web application deepfake face detection that employs a deep learning architecture i.e XceptionNet. However, we have trained two models, XceptionNet and ResNet. But XceptionNet has won as it has an AUC slightly greater than that of ResNet and accuracy. Our project provides full stack web application for users who are non-coders, those who don’t know coding, they can also use our platform. Even if they don’t know the coding, they can use our platform to detect whether the image is fake or real. The model is trained on data such as a hybrid multi-source dataset strategy using real images from CelebA, Latent Diffusion Models (LFW), and synthetic samples from StyleGAN and stable- diffusion. The experimental evaluation shows that XceptionNet outperforms ResNet152V2 with a testing accuracy of 93%. The system runs on a secure Flask and React.js setup. It uses a RAM-only inference pipeline that protects privacy.
Mankanti Hazari Adithya Prasad, Gujja Goutham, Jangiti Nithish, Gurjala Hardhik, Mohammad Abdul Rahman
AI-Driven Cyber-Resilient Cloud-Native Architecture for Autonomous Enterprise Systems and Secure Digital Ecosystems
The rapid advancement of digital technologies and cloud computing has enabled enterprises to develop autonomous systems capable of managing complex operations with minimal human intervention. However, this transformation has also increased cybersecurity risks due to highly distributed infrastructures, interconnected services, and continuously evolving cyber threats. To address these challenges, this paper proposes an AI-driven cyber-resilient cloud-native architecture designed to support autonomous enterprise systems and secure digital ecosystems. The proposed framework integrates artificial intelligence, cloud-native technologies, automated threat detection, and adaptive security mechanisms to ensure system resilience against cyberattacks. By leveraging machine learning models for predictive threat analysis and automated incident response, the architecture strengthens enterprise cybersecurity while maintaining system availability and operational continuity. The framework emphasizes proactive defense strategies, real-time monitoring, and self-healing capabilities that enable organizations to maintain secure and resilient digital environments.
Automating Incident Triage and Root Cause Intelligence Through Large Language Model–Driven Correlation of System Logs and Operational Metrics in Large-Scale Distributed Environments
The increasing scale and complexity of distributed computing environments have intensified the difficulty of timely incident triage and accurate root cause identification, as operators must reason across high volume system logs and heterogeneous operational metrics under severe time constraints. This work addresses the research problem of how incident response can be transformed from manual, heuristic driven practices into an intelligent and automated process capable of semantic understanding and contextual reasoning. The objective is to investigate how large language model driven analysis can be systematically applied to correlate logs and metrics for reliable incident triage in production scale systems. A mixed methodological approach is adopted, combining architectural design, qualitative analysis of operational workflows, and quantitative evaluation of diagnostic efficiency across representative enterprise scenarios. The proposed framework introduces a novel correlation pipeline that leverages language model based contextual abstraction to unify unstructured log streams and structured metrics into coherent incident narratives. Empirical patterns suggest substantial reductions in triage time, improved diagnostic precision, and lower cognitive burden on reliability engineers when compared with traditional rule based and statistical techniques. The findings demonstrate that language model driven reasoning enables a shift from reactive alert handling toward proactive root cause intelligence. The primary contribution lies in articulating a principled foundation for integrating large language models into observability and incident management systems, bridging academic advances in machine intelligence with real world operational demands. The study concludes that automated, semantics aware triage represents a critical advancement for scalable reliability engineering, with significant implications for future research and enterprise operations in large scale distributed environments.
Streaming-First Enterprise Decision Systems: Architectural Evolution from Batch Dataflows to Stateful, Exactly-Once Real-Time Processing
Enterprise decision systems increasingly depend on real-time data streams to enable operational intelligence, fraud detection, predictive maintenance, dynamic pricing, supply chain optimization, and adaptive customer engagement across digital platforms. The architectural evolution from batch-oriented distributed processing models to unified, stateful stream-processing engines has fundamentally reshaped how enterprises design, deploy, and scale mission-critical systems. Early distributed data systems such as Google’s MapReduce and Google File System established the principles of large-scale data partitioning, fault-tolerant execution, and horizontal scalability, forming the conceptual backbone for modern data infrastructure. Building upon these foundations, streaming platforms such as Apache Kafka introduced durable, distributed log-based messaging; Apache Spark advanced micro-batch stream computation; Apache Flink enabled true event-driven, stateful processing with consistent checkpointing; and Google’s MillWheel demonstrated low-latency, exactly-once semantics at Internet scale. Together, these innovations converged to form a cohesive architectural paradigm in which ingestion layers, stateful stream processors, scalable storage backends, and real-time serving components operate as an integrated decision fabric. By examining key architectural diagrams and seminal studies, this article synthesizes these developments into a unified blueprint for modern enterprise decision systems, highlighting core design principles for scalability, deterministic state management, event-time correctness, resilience under failure, elasticity under fluctuating workloads, and the practical realization of exactly-once processing guarantees in distributed environments.
Secure SAP Microservices and DevOps-Driven Continuous Integration Framework for Cloud-Based Enterprise Platforms
Modern enterprises increasingly rely on cloud-native technologies and SAP-based enterprise resource planning systems to manage large-scale business operations. As organizations adopt digital transformation strategies, integrating secure microservices architectures with DevOps-driven continuous integration frameworks has become essential for maintaining operational efficiency, scalability, and cybersecurity resilience. Traditional monolithic SAP deployments often struggle to support rapid innovation, secure system integration, and automated deployment pipelines required by modern enterprise platforms. This research proposes a secure SAP microservices and DevOps-driven continuous integration framework designed to enhance the scalability, reliability, and security of cloud-based enterprise platforms. The proposed architecture decomposes SAP services into modular microservices that can be independently developed, deployed, and managed through automated DevOps pipelines. Continuous integration and continuous delivery (CI/CD) mechanisms enable automated testing, code validation, security scanning, and deployment orchestration across distributed cloud environments. The framework also incorporates security mechanisms including identity management, container security, encrypted communication protocols, and policy-driven access control systems. Experimental evaluation demonstrates that the integration of microservices architecture with DevOps automation significantly improves enterprise deployment efficiency, reduces software delivery time, and enhances system security monitoring capabilities. The results confirm that organizations adopting the proposed framework can achieve improved scalability, faster application development cycles, and more resilient cloud-based enterprise systems.
Containerized Microservices Architecture
Machine Learning and MLOps-Based Multi-Cloud Data Platforms for Scalable Enterprise Analytics and Banking Risk Intelligence
The banking and financial sectors are increasingly dependent on large-scale data analytics to drive operational efficiency, risk management, and strategic decision-making. Multi-cloud data platforms enable enterprises to harness distributed computing resources, manage diverse datasets, and deploy scalable analytics pipelines across heterogeneous cloud environments. However, integrating machine learning (ML) models, ensuring reproducibility, and maintaining operational efficiency in multi-cloud settings present significant challenges. This research proposes a machine learning and MLOps-driven multi-cloud data platform designed for scalable enterprise analytics and banking risk intelligence. The framework leverages distributed data storage, automated model deployment pipelines, and cross-cloud orchestration to enable efficient data processing, real-time analytics, and predictive risk assessment. MLOps practices ensure model versioning, continuous integration, automated testing, and reproducibility across cloud platforms, reducing deployment errors and operational overhead. The proposed architecture supports risk intelligence by integrating ML models capable of detecting fraudulent transactions, forecasting market risk, and identifying operational vulnerabilities. Multi-cloud orchestration ensures scalability, redundancy, and compliance with regulatory frameworks such as Basel III and GDPR. The study highlights the benefits of combining machine learning, MLOps, and multi-cloud platforms for enterprise-scale analytics while discussing challenges including system complexity, data governance, and operational security.
Scalable Cloud Data Integration Models for Smart Healthcare Information Exchange
Modern healthcare systems are evolving into smart and connected service networks, organized around people and enabled by information technology and data for improving lifestyle, health, and wellness. As the amount of data in smart healthcare applications keeps increasing, privacy, security, and legal concerns are becoming critical for sharing sensitive healthcare information across organizations, especially as organizations migrate their services to either hybrid or full cloud solutions. Cloud computing plays a major role in smart healthcare application systems by providing data infrastructures, architectures, and services with efficient data storage and processing capabilities. Integrating heterogeneous data to and from various cloud data services constitutes a significant challenge in cloud-based healthcare applications, particularly in healthcare information exchange. This section presents scalable, evidence-based, and formally structured analysis of cloud data integration for smart healthcare data exchange. The core concepts and operating objectives of cloud data integration for smart healthcare information exchange are defined, the key architectural paradigms are identified, and the security, privacy, and legal considerations associated with the integration of sensitive healthcare information across organizations are addressed. Seeking cost-efficient storage and processing solutions, the discussion identifies suitable data integration models, including cloud-native data lakehouse architectures and event-driven, stream-processing data pipelines. The analysis also includes issues related to data quality, performance and scalability, and applicable implementation strategies. Such an encompassing view supports the design and management of cloud-based data integration solutions that address the scalable data-related requirements of organizations in smart healthcare ecosystems.
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
AI-Powered Price Prediction for Agriculture Markets
Farmers, traders and policymakers face uncertainty due to fluctuation in the price of agricultural products and it has normally resulted in financial losses and inferior market decisions. The conventional approaches of forecasting crop prices use past trends and manual forecasting, which are not accurate and adaptable to changing market conditions. The current paper will suggest an AI-based Agricultural Price Prediction System based on the application of Machine Learning, past market data, weather conditions, demand and supply analysis, and seasonal changes to predict the prices of crops. The system gives the farmers predictions of their prices in advance so that they may make better decisions in selling their produce, they will not depend on middlemen and their income will be more stable. The given model will promote the transparency of agricultural markets and make evidence-based decisions.
SK.Adil Basha, V.Sai Balarama Krishna, S.Shiva Shanker, R.Sravya
Adaptive Risk Aware AI Analytics Framework for Enterprise Financial Healthcare and Socio Economic Intelligence
Adaptive Risk-Aware Artificial Intelligence (AI) analytics frameworks are becoming essential for modern enterprises that operate within complex financial, healthcare, and socio-economic ecosystems. Organizations increasingly rely on large volumes of heterogeneous data generated from digital transactions, electronic health records, market indicators, and social systems. Traditional analytics models often lack the ability to dynamically adapt to emerging risks, uncertain environments, and evolving regulatory requirements. This research proposes an adaptive risk-aware AI analytics framework that integrates machine learning, predictive modeling, and real-time risk assessment to enhance enterprise decision-making across multiple sectors. The framework focuses on combining data integration, risk detection, contextual learning, and adaptive decision intelligence to support strategic and operational planning. By incorporating financial risk modeling, healthcare predictive analytics, and socio-economic intelligence systems, the proposed framework enables enterprises to detect anomalies, predict future risks, and respond proactively to changing conditions. The architecture emphasizes explainable AI, data governance, and continuous model adaptation to maintain reliability and transparency. The research methodology employs multi-domain data analytics, algorithmic risk evaluation, and simulation-based validation. The results demonstrate that adaptive AI analytics significantly improves risk detection accuracy, resource allocation efficiency, and policy planning effectiveness. The proposed framework contributes to the development of intelligent enterprise ecosystems capable of managing complex risks in dynamic socio-economic environments.
AI-Powered Data Engineering Frameworks for Smart Manufacturing Quality Control
AI-Powered Data Engineering Frameworks for Smart Manufacturing Quality Control presents an evidence-based, formal analysis of AI methods, data pipelines, and governance to improve defect detection and process reliability in smart manufacturing quality control. The contributions cover data engineering prerequisites—including data sources, quality requirements, acquisition approaches, ingestion methods, latency considerations, and integration—together with key decision-supporting AI techniques, a comprehensive system architecture for end-to-end quality control, and high-level data governance requirements. Exploiting artificial intelligence (AI) to enhance manufacturer-automated quality control processes enables self-driving factories with reduced defect rates. AI methods are implemented for defect detection, correlation, and root-cause forecasting, closing the gaps between Machine Learning, Big Data, and IoT. Data quality proves decisive for these operations, raising specialized Data Engineering requirements across the entire analytical pipeline and including Quality Control Data Engineering-as-a-Service. By framing the analysis within the broader context of smart factory data engineering, a comprehensive set of Quality Control data quality requirements emerges and combinations of supervised, unsupervised, and time series methods are explored to tackle both defect detection and repair procedure prediction.
Manufacturing Data Engineering Frameworks
Quality Control Data Engineering-as-a-Service
Supervised and Unsupervised Learning Integration
Data Quality Requirements in Smart Factories
Intelligent Production Process Optimization