International Journal of Computer Technology and Electronics Communication
Integrating Machine Learning with Cloud Computing for Intelligent and Resilient Systems with Analytics
The integration of machine learning (ML) with cloud computing has emerged as a transformative approach for developing intelligent and resilient systems capable of advanced analytics. Cloud platforms provide scalable infrastructure and computational resources, while ML algorithms enable systems to learn from data, adapt to changing conditions, and make informed decisions. This synergy supports a wide range of applications, including predictive analytics, anomaly detection, and automated system optimization. However, the increasing reliance on cloud-based ML systems introduces challenges related to data security, system reliability, and performance under dynamic workloads. This study explores the architectural design, implementation strategies, and best practices for integrating ML with cloud computing to build intelligent and resilient systems. It examines key components such as distributed data processing, real-time analytics pipelines, and fault-tolerant architectures. The research also highlights the role of resilience mechanisms, including redundancy, load balancing, and self-healing systems, in ensuring continuous operation. By leveraging ML-driven analytics within cloud environments, organizations can enhance system performance, improve decision-making, and ensure robustness against failures and uncertainties. The study concludes that effective integration of ML and cloud technologies is essential for building next-generation intelligent systems.
Adoption Drivers of Indigenous Semiconductor chips for IoT in India: An Exploratory Study
India’s strategic objective of achieving self-reliance in semiconductor technology has gained urgency following global supply-chain disruptions and the rapid expansion of Internet of Things (IoT) applications. Despite hosting extensive semiconductor design capabilities, India remains dependent on imported microprocessors for most electronic systems. This study examines the factors shaping the acceptance of indigenously developed semiconductor chips for IoT applications in India, with specific reference to the SHAKTI processor ecosystem. Using a quantitatively supported grounded theory approach, data were collected from industry experts, policymakers, manufacturers, and ecosystem stakeholders through interviews, surveys, and document analysis. The findings indicate that acceptance is driven by four interrelated dimensions: design capability, manufacturing readiness, go-to-market strategy, and policy support. While India demonstrates strong design competence and policy intent, gaps persist in manufacturing scale, supply-chain robustness, and market confidence. The study contributes to the limited empirical literature on semiconductor ecosystem development by proposing a holistic, non-technical framework to accelerate adoption of indigenous processors in IoT deployments.
Cross-Platform Automation Strategy for Hybrid OTT and SaaS Applications
In our investigation, we have a cross-platform automation approach dedicated to the hybrid system of Over-The-Top (OTT) and Software-as-a-Service (SaaS) in terms of mobile, web, and embedded environment. As a variety of device ecosystems and microservice-based backend have flourished around, verifying end-to-end functionalities has become one of the primary engineering issues to overcome on a regular basis. The way we do it, instead of fully abstracted platform-less tests, is to have a framework of tests that is layered on top of platform-specific test modules, so high reuse of test scripts, low redundancy and easy regressions. CI/CD pipelines make it possible to parallelize and scale up the execution and eliminate release cycle bottlenecks. We assess the deployment strategy in real life scenarios of Android TV, iOS, browsers and web portals architecture, and SaaS portal architecture. The results have shown a high level of test development productivity (up to 45 percent), test result (58 percent quicker) and failure identification (73 percent quicker). Moreover, we realized quantitative business outcome, such as faster release speed and lower-level of leak defect. The plan also addresses platform-specific platform stability problems with the help of adaptive locator strategies and retry thinking. With the verification of the multi-modal workflows, including mobile-to-OTT control and synchronized playback, our plan is appropriate in the hybrid product setting. The suggested architecture and automation system can serve as the basis of future validation systems which would help in merging fragmented device landscapes into a single quality assurance approach.
Executable Data Contracts for Reliable AI Pipelines
An executable Data Contract (EDC) is an emerging paradigm of data architecture in assuring data quality, compliance and interoperability of contemporary data ecologies. In contrast to more traditional, static contracts that exist only as documents defining schema and validation rules, EDCs contain that logic as part of a runnable, executable program that can fit directly into data pipelines and systems of record. This paper measures the operational, compliance and performance costs of deploying EDCs on a heterogeneous data landscape integrating cloud-native warehouses, API-centric integration and regulated use-cases, like finance and healthcare. We determined how efficiencies in validation, reduction of error, compliance with regulation, and cost minimization are looked at using a mixed-methods approach that comprises of both empirical measurement and simulation-based stress tests as well as interviews with stakeholders. The results will bring findings that adoption of EDC will reduce data-related defects 62 to 74 percent, pipeline set up approval time 35 to 42 percent, and compliance scores to 15 percent. Nevertheless, its implementation does not occur without some of the obstacles, such as the complexity of primary development, investment required in integrating them with the legacy systems, and the alignment of governances across business units. The study then comes to a conclusion that EDCs have most potency when used together with automated CI/CD validation pipelines, schema version control and compliance aware orchestration layers. The maturity scheme provided is a phased plan of using EDCs that companies can follow in order to balance performance enhancement against manageability and governance. These findings can serve as an empirical basis on which an adequate effort to roll out the data governance policies to a real time environment can be based with minimal friction between the engineering and compliance groups
An executable Data Contract (EDC) is an emerging paradigm of data architecture in assuring data quality, compliance and interoperability of contemporary data ecologies. In contrast to more traditional, static contracts that exist only as documents defining schema and validation rules, EDCs contain that logic as part of a runnable, executable program that can fit directly into data pipelines and systems of record. This paper measures the operational, compliance and performance costs of deploying EDCs on a heterogeneous data landscape integrating cloud-native warehouses, API-centric integration and regulated use-cases, like finance and healthcare. We determined how efficiencies in validation, reduction of error, compliance with regulation, and cost minimization are looked at using a mixed-methods approach that comprises of both empirical measurement and simulation-based stress tests as well as interviews with stakeholders. The results will bring findings that adoption of EDC will reduce data-related defects 62 to 74 percent, pipeline set up approval time 35 to 42 percent, and compliance scores to 15 percent. Nevertheless, its implementation does not occur without some of the obstacles, such as the complexity of primary development, investment required in integrating them with the legacy systems, and the alignment of governances across business units. The study then comes to a conclusion that EDCs have most potency when used together with automated CI/CD validation pipelines, schema version control and compliance aware orchestration layers. The maturity scheme provided is a phased plan of using EDCs that companies can follow in order to balance performance enhancement against manageability and governance. These findings can serve as an empirical basis on which an adequate effort to roll out the data governance policies to a real time environment can be based with minimal friction between the engineering and compliance groups.
Event-Driven Architectures for Real-Time Data Synchronization: Lessons from Multi-Region Cloud Deployments
The current paper examines the role of event-driven architectures (EDA) to assist in ensuring the synchronization of real-time data in various locations of the cloud. The current global systems have numerous challenges such as delays in information, duplicate and regional interruptions in case information is modified at various points simultaneously. This work is created with the help of AWS serverless primaries (Lambda, Kinesis, DynamoDB Streams) and provides an event-driven model of synchronization that manages millions of users. The experiment is an analysis of the latency, throughput, and error rates over the regions in the process of live replication. It further verifies idempotence event handling and cross region restoration during the event of failures. Findings indicate that pipelines supported by an event can continue with low latency rates, high availability, and a proper level of replication regardless of network failures. There are also quantitative results, small code samples, and specific visualization charts of the paper to explain the behavior of the system in a clear manner. Production-grade implementations are tweaked into lessons that can be generalized into a blueprint that can be implemented in other enterprises. This study can be used to construct stronger, smoother, and non-conformist data pipelines when using a distributed cloud system.
Transformative AI Framework for Data Mining and Federated Learning in Financial Intelligence and Smart Healthcare
The rapid growth of data-intensive applications in financial services and healthcare has necessitated advanced computational frameworks that ensure data security, privacy, and efficiency. Traditional centralized machine learning approaches often require aggregating sensitive data into a single location, posing significant privacy and regulatory challenges. Federated Learning (FL) emerges as a paradigm that allows multiple decentralized entities to collaboratively train machine learning models while keeping raw data localized. This study proposes an advanced AI and federated learning framework tailored for secure distributed systems in financial services and healthcare. The framework leverages state-of-the-art AI techniques, including deep learning, reinforcement learning, and privacy-preserving mechanisms like differential privacy and secure multi-party computation. By integrating FL with blockchain-based auditability and encryption protocols, the system ensures robust data confidentiality and integrity. The proposed framework is evaluated across real-world healthcare and financial datasets, demonstrating significant improvements in model accuracy, reduced communication overhead, and compliance with privacy regulations such as GDPR and HIPAA. The results indicate that adopting this federated approach can enable institutions to harness distributed intelligence while mitigating risks associated with data breaches and regulatory non-compliance, paving the way for next-generation secure, collaborative AI in sensitive domains.
Intelligent AI-Based Fraud Detection Framework for Real-Time Financial Transactions with Predictive Analytics
The increasing use of digital transactions has contributed to the rising number of frauds in the financial systems and acts as a great threat to individuals and organizations. This project is a smart AI-driven fraud detector system that will be developed to track and analyze live financial transactions through predictive analytics. The structure incorporates various machine learning models, such as anomaly detection, classification models, and time series forecast models, to detect suspicious behavior, as well as possible fraud patterns in real time. To reduce inaccuracy and response time, the recommended system adopts a layered framework, which involves data collection and preprocessing, feature extraction, fraud detection, and mitigation. The predictive analytics component of the system can be utilized in order to incessantly understand the patterns of fraud using historical transaction data, which can aid in achieving broader results in terms of detecting new, never-before-seen cases of frauds. With such AI-based frameworks, the financial institutions will be in a better position to minimize the time taken to respond to any fraudulent transaction and cut down on the monetary losses incurred and increase customer confidence. The paper determines the performance of the framework in terms of accuracy, precision, recall and real-time detection capabilities and proves that the framework is effective when applied in the real world. The findings have shown that the framework is superior to the conventional approaches of detecting frauds and offers a very powerful solution in securing financial transactions. This study identifies the opportunities of AI and predictive analytics to transform the fraud detection process in the financial sector and make it more secure and efficient.
Pattern-Based Stream Enrichment and Aggregation Architectures for Low-Latency Financial Data Systems
Modern financial data systems operate under stringent requirements of low latency, high throughput, fault tolerance, and correctness, driven by the real-time nature of market activity and regulatory oversight. Applications such as market data dissemination, fraud detection, real-time risk assessment, compliance monitoring, and settlement processing increasingly depend on continuous event streams rather than static datasets, as delayed or inconsistent processing can translate directly into financial loss or regulatory exposure. Within these environments, stream enrichment and aggregation form the computational core, integrating high-velocity event flows with reference datasets such as instrument metadata, customer profiles, and risk parameters, while producing higher-order insights through windowed, stateful, and temporal computations. This paper presents a pattern-based approach to stream enrichment and aggregation tailored specifically for financial systems, drawing on foundational data stream management systems (DSMS) such as Aurora and Borealis and extending their principles through modern distributed stream processing engines like Apache Flink. We systematically classify reusable enrichment and aggregation patterns, analyze their architectural and operational implications, and examine state management, fault recovery, and correctness guarantees under latency-sensitive workloads. By synthesizing early DSMS research from 2000-2005 with contemporary stream processing advancements from 2011-2018, the paper provides a structured and historically grounded framework for designing scalable, resilient, and maintainable financial streaming pipelines capable of meeting both performance and reliability demand.
Master Data Management as a Strategic Foundation for Enterprise Consistency: Frameworks, Architectures, and Governance Practices
Enterprise systems today operate across distributed, cloud-enabled, and often heterogeneous environments, generating massive volumes of transactional, reference, and operational data that flow continuously between ERP, CRM, supply chain, finance, and analytics platforms. In the absence of a coherent strategy to manage master entities—such as customers, products, suppliers, employees, and accounts—organizations encounter fragmented data silos, duplicate records, inconsistent hierarchies, reconciliation overhead, regulatory exposure, and diminished analytical reliability. These inconsistencies not only impair operational efficiency but also undermine executive decision-making, digital transformation initiatives, and customer experience management. Master Data Management (MDM) has therefore emerged as a strategic discipline that integrates governance, data quality management, metadata control, and architectural standardization to create authoritative “golden records” and ensure enterprise-wide consistency. By aligning business ownership with technological enablement, MDM frameworks support data stewardship, survivorship rules, lifecycle governance, and synchronization mechanisms across systems. This paper examines foundational MDM concepts, architectural patterns such as registry, consolidation, coexistence, and transactional hubs, governance and stewardship models, and structured implementation strategies. Drawing on industry white papers from IBM, Informatica, and Oracle, early analyst research from IDC, and academic contributions published between 2000 and 2020, the study synthesizes conceptual clarification, architectural illustration, and process modeling to propose a phased and sustainable strategic framework for achieving enterprise consistency through disciplined MDM adoption.
Building a Resilient ODS Integrating Graph Algorithms and Azure for Enterprise Analytics
The establishment of a contemporary Operational Data Store (ODS) design and architecture incorporates investment, financial, operational, and market-related datasets across various countries into one central repository at a multinational Private Equity Firm. The architecture incorporates an event-driven and graph-aware design methodology with a selective refresh approach for overcoming challenges related to very large datasets, multiple dependencies between financial indicators, and real-time updating and synching of data. By utilizing various Azure-native services paired with superior graph algorithms, the Platform offers more scalability around data management, better manages intelligent re-calculation capabilities, and provides near- real-time analytics. By offering a consolidated view in terms of corporate reporting this architecture has also significantly improved overall operational efficiency while simultaneously lowering costs and increasing the speed at which decisions can be made. Additionally, this research provides the groundwork for further developing automation and predictive analytic capabilities while emphasizing the value of creating innovative ODS designs that revolutionize the way in which financial/market-related data is managed.
Predictive Analytics and Machine Learning Framework for Secure Enterprise Platforms and Intelligent Infrastructure Management
Predictive analytics and machine learning (ML) have emerged as essential technologies for modern enterprise platforms, enabling organizations to optimize operations, enhance security, and manage infrastructure intelligently. This paper proposes a comprehensive framework that integrates predictive analytics and ML algorithms to anticipate operational bottlenecks, detect security threats, and automate infrastructure management in enterprise environments. The framework leverages historical data, real-time monitoring, and advanced statistical models to provide actionable insights, improve decision-making, and reduce downtime. Security considerations are embedded within the ML pipeline to ensure data confidentiality, integrity, and compliance with organizational policies. Additionally, the framework supports scalability, interoperability, and integration with existing enterprise systems, enabling a seamless transition to intelligent infrastructure management. Case studies and simulation results demonstrate the efficacy of the proposed approach in enhancing predictive accuracy, operational efficiency, and proactive threat mitigation. This framework provides a roadmap for organizations seeking to adopt data-driven strategies to manage enterprise platforms securely and efficiently. Future research directions include incorporating adaptive learning, edge computing, and explainable AI to improve model interpretability and responsiveness in dynamic enterprise environments.
Next Generation Intelligent Cloud Framework for Secure Digital Banking Healthcare and Government Infrastructure
The rapid digital transformation in banking, healthcare, and government sectors has accelerated the adoption of cloud computing to support scalable, flexible, and resilient services. However, these infrastructures are increasingly exposed to cyber threats, regulatory compliance challenges, and complex operational demands. This research proposes a next-generation intelligent cloud framework designed to provide secure, efficient, and reliable services for digital banking, healthcare, and government infrastructures. The framework integrates artificial intelligence (AI), machine learning (ML), and advanced security mechanisms to enable predictive threat detection, dynamic resource management, and adaptive risk mitigation. Core components include AI-driven anomaly detection, multi-layer encryption, identity and access management, and real-time monitoring. The proposed framework is evaluated using simulated digital banking, healthcare, and government datasets, demonstrating improvements in security, service availability, and operational efficiency compared to traditional cloud architectures. Experimental results indicate a reduction in potential security breaches, enhanced compliance with regulatory standards such as GDPR, HIPAA, and PCI DSS, and optimized resource utilization. This research provides a comprehensive blueprint for deploying intelligent, secure, and resilient cloud systems capable of supporting mission-critical digital services in highly regulated environments.
Cloud-Based Big Data Governance Frameworks for Digital Public Service Platforms
Governments are often viewed by citizens and businesses as the natural owners of public data that are generated or collected in their jurisdictions. It is expected that such data assets, if governed properly, can facilitate the provision of better services, enable data-driven policymaking, foster innovation, and drive new economic opportunities. However, big data present new and complicated challenges. Recent studies indicate data governance has become one of the most puzzling issues for governments pursuing digital transformation, which necessitates the establishment of a governance framework. Indeed, evidence-based guidance to inform the establishment of a big data governance framework for digital public service platforms on cloud infrastructure using a governance-as-a-service model is needed. An analysis of the different stakeholders that contribute to the operation of digital public service platforms on a cloud infrastructure reveals that the primary goal is to establish the data governance framework. This framework aims to protect information security and ensure data can be reused and shared with low risk. Following a formal structure, four cloud-based big data governance frameworks adopted by governments are presented: the Cloud-Based Big Data Governance Framework, which targets big data ecosystems; the Cloud-Based Big Data Governance White Paper, which informs the operation of data exchanges; the Cloud-Based Smart City Data Governance Framework, which addresses the establishment of key data resources; and the Cloud-Based Data-Driven Economy Toolkit, which supports the proactive sharing of public data. Together, they form a roadmap for establishing a complete big data governance framework for digital public service platforms in a cloud environment.
Credit Card Fraud Detection System Using Machine Learning
The rapid growth of online payment systems has made credit cards a widely used more of digital transactions. Along with this growth, incidents of credit card fraud have increased, leading to financial losses for banks and reduced confidence among customers. Conventional fraud detection approaches are mostly based on predefined rules, which are often ineffective in identifying newly emerging fraud patterns, particularly when fraudulent transactions form only a small portion of the overall data. To address this challenge, this study applies machine learning techniques for identifying fraudulent credit card transactions. A major issue in this domain is the highly imbalanced nature of transaction data, where legitimate transaction significantly outnumber fraudulent ones. To reduce the impact of data imbalance, the Synthetic Minority Over-Sampling Technique (SMOTE) is used to generate additional fraud samples and enhance model training. Multiple machine learning classifiers are developed to learn transaction patterns and distinguish between genuine and fraudulent activities. Model performance is assessed using recall, F1-Score, and the Area under the Precision-Recall Curve (AUPRC), as these metrics provide a more reliable evaluation for imbalanced datasets. Experimental analysis indicates that the Random Forest classifier outperforms other models, achieving an accuracy of 99.95%. The findings demonstrate that machine learning-based approaches can significantly enhance fraud detection systems and support effective real-time monitoring of credit card transactions.
Mandati Sindhu, Gujja Srisailam, Ganji Vijay, Katika Komala, Jadav Sanjana
Cloud-Based File Sharing App with Data Security
In today's digital era, data sharing and collaboration have become essential components of both personal and organizational workflows. Traditional file-sharing methods such as USB drives, emails, and local networks are often inefficient, limited in scalability, and vulnerable to data breaches. To overcome these challenges, cloud-based file sharing applications have emerged as a reliable secure, and accessible solution for storing and exchanging digital information. A cloud- based file sharing system alows users to upload, store, and share files over the internet through a centralized cloud server. It enables real-time access to data from any location, at any time, using any device connected to the internet. This flexibility enhances productivity and collaboration among users, especially in distributed and remote environments. However, with the growing volume of sensitive data being shared over the cloud, data security has become a major concern. Unauthorized access, data leakage, and cyberattacks can compromise user privacy and organizational integrity. Therefore, integrating robust security mechanisms-- such as end-to-end encryption, user authentication, role-based access control, and secure data transfer protocols (like HTTPS and SSL/TLS)- is critical to ensure confidentiality, integrity, and availability of shared data The proposed major project aims to design and implement a Cloud-Based File Sharing Application with Data Security, providing users with a secure and user-friendly platform to upload, download, and share files. The system will utilize cloud storage for scalability and employ encryption techniques to safeguard files from unauthorized access. Additionally, the application will include features like file versioning, access permissions, and activity logs to enhance transparency and control over data usage. This project not only addresses the need for efficient and secure data sharing but also contributes to the growing field of cloud computing and cybersecurity by demonstrating how advanced technologies can be integrated to protect user data in a cloud environment.
Gayathri Chenchala, Adithya Boini, Sai Charan Challa, Sai Kumar Chinningu, Dasaroju Damodhar Chary
AI-Driven Intelligent Ticket Classification for Enterprise IT Service Management
In a typical IT service desk, the volume of incoming service tickets can exceed several thousand per week. Handling these tickets relies on an underlying classification or routing process that maps each ticket to qualified expert resources for resolution. The classification can be based on a predefined taxonomy, which provides a multi-level hierarchy of various types of IT service requests and distinguishes between incidents, problems, changes, queries, and other service requests. A traditional approach for ticket classification is based on manually crafted rules. However, such a rules-based approach is not always feasible, and classifications can frequently need to account for very rare or less frequently seen tickets. Even a rules-based classifier cannot always guarantee the effective usage of resources. Machine learning has increasingly been used for automating the classification operation and shifting the IT service desk to a model-driven setup, where the classical rules-based approach is enhanced by a learning-based operation that learns the mapping function. AI-driven intelligent classifiers can cater to frequently seen tickets and can learn either a one-vs-each approach for every type of service request or process the ticket in a multi-task learning setup.
Federated AI Architectures for Secure Multi-Organization Healthcare Data Analysis
The difficulties of working in sectors with strict privacy constraints, such as healthcare, result from the fact that data is separated and distributed across multiple sources that are unable to exchange it. The solution to this dilemma is to allow multiple data owners to collaborate on a joint task by using shared/global models while keeping the data decentralized. Three paradigms of federated machine learning are proposed: horizontal federated learning, vertical federated learning, and federated transfer learning. Federated learning processes and architectures with privacy-preserving properties that also address data provenance, access control, and interoperability. Integrating heterogeneous distributed data sources while maintaining privacy is a major challenge for modern data and AI analysis. Sensitive data in particular, such as healthcare data, privacy is guaranteed by law. Therefore, many healthcare data sources are available based on these data sharing and data monetization are a difficult integration task. Multiple organizations are willing to collaborate by sharing insights from their private datasets, but without sharing the original datasets because healthcare data is highly sensitive. However, existing guidelines permit the use of healthcare data in Europe and America only for either research or health benefits, and not even for commercial use. Nevertheless, several companies are attempting to break this barrier by using fake-generated data for AI modeling purposes. As a result, three main criteria must be considered during the establishment of collaboration for multi-organization AI data modeling: privacy, security, and monitoring.
Machine Learning–Enabled Security and Governance Framework for SAP-Based Cloud-Native Enterprise Systems
The rapid digital transformation of enterprises has accelerated the adoption of cloud-native technologies and SAP-based enterprise platforms. Organizations increasingly rely on SAP systems deployed on cloud infrastructures to manage critical business operations such as finance, supply chain management, human resources, and customer engagement. However, the growing complexity of cloud-native architectures introduces significant challenges in ensuring security, governance, compliance, and risk management. Traditional rule-based security mechanisms often struggle to detect sophisticated cyber threats and anomalous activities in dynamic enterprise environments. This research proposes a Machine Learning–Enabled Security and Governance Framework designed specifically for SAP-based cloud-native enterprise systems. The framework integrates machine learning models with cloud-native security mechanisms to enable intelligent threat detection, automated governance monitoring, compliance enforcement, and predictive risk analysis. The architecture incorporates multiple layers including data acquisition, machine learning analytics, security orchestration, and governance automation. By leveraging anomaly detection algorithms, predictive analytics, and real-time monitoring, the proposed framework enhances enterprise resilience against cyber threats while maintaining regulatory compliance. The study demonstrates how machine learning techniques can improve visibility across SAP environments, detect insider threats, prevent unauthorized access, and optimize governance policies. The proposed framework provides a scalable and adaptive security model that aligns with modern enterprise digital transformation strategies and cloud-native architectures.
Machine Learning Enabled Governance Framework for Autonomous Enterprise Platforms and Intelligent Data Ecosystems
The rapid expansion of digital technologies has led to the emergence of autonomous enterprise platforms and intelligent data ecosystems that rely heavily on advanced analytics, automation, and artificial intelligence. As organizations increasingly adopt data-driven decision-making processes, the need for robust governance frameworks that ensure transparency, accountability, security, and ethical use of machine learning systems has become critical. This research proposes a Machine Learning Enabled Governance Framework designed to manage and regulate autonomous enterprise platforms and intelligent data ecosystems effectively. The framework integrates machine learning models with governance mechanisms such as policy enforcement, compliance monitoring, data quality management, and risk assessment to support responsible data utilization and automated decision-making. The proposed governance architecture enables enterprises to monitor AI-driven systems continuously, detect anomalies in data operations, and ensure regulatory compliance across complex digital infrastructures. By incorporating automated auditing, explainable AI mechanisms, and policy-based access controls, the framework promotes transparency and trust in enterprise-level machine learning applications. The research methodology combines conceptual modeling, comparative analysis of existing governance frameworks, and simulation-based evaluation to validate the effectiveness of the proposed model. The findings indicate that machine learning-enabled governance frameworks can significantly enhance operational efficiency, reduce compliance risks, and improve the reliability of autonomous enterprise systems while ensuring responsible data management within intelligent data ecosystems.
Carbon-Aware Kubernetes Scheduling Using Deep Reinforcement Learning for Mixed Batch and Latency-Sensitive Workloads
Carbon emissions from cloud data centers vary significantly with workload placement owing to regional and temporal differences in carbon electricity intensity. Mainstream Kubernetes schedulers remain entirely agnostic to these differences, systematically missing opportunities for emission reduction. This paper presents a carbon-aware Kubernetes scheduling framework driven by Proximal Policy Optimization (PPO), a deep reinforcement learning algorithm chosen for its stable policy gradient updates under non-stationary carbon signals. The scheduling problem is formulated as a Markov Decision Process in which the agent jointly optimizes carbon efficiency, SLA compliance, and resource utilization. Workloads are classified into latency-sensitive services and delay-tolerant batch jobs, with asymmetric reward weighting that strongly penalizes SLA violations for the former while prioritizing carbon placement for the latter. Large-scale simulation and Azure Kubernetes Service testbed experiments show that the proposed scheduler reduces mean carbon emissions per workload by 28% compared with the default scheduler and by 15% compared with a rule-based carbon-and-SLA-aware heuristic, while holding SLA violation rates below 1.2%. Critically, the PPO scheduler also reduces carbon emission variance by 49% under stochastic conditions, providing more predictable environmental performance than any baseline—a property with direct operational value for sustainability commitments.
Cyber-Resilient AI-Driven Enterprise Cloud Framework for Financial Healthcare Analytics Regulatory Compliance and Automation
The convergence of cloud computing, artificial intelligence, and enterprise digital transformation has created unprecedented opportunities for enhancing operational efficiency, security, and data-driven decision-making. Enterprises in sensitive sectors such as finance and healthcare face increasing demands for secure, scalable, and compliant cloud infrastructures. Traditional enterprise systems often struggle to provide resilience against cyber threats, ensure regulatory compliance, and enable intelligent automation at scale. This research proposes a cyber-resilient AI-driven enterprise cloud framework designed to integrate financial systems, healthcare analytics platforms, regulatory compliance mechanisms, and intelligent automation processes. The proposed framework leverages artificial intelligence for predictive threat detection, anomaly detection, and automated decision-making, while cloud-native technologies provide scalability, fault tolerance, and high availability. Advanced security measures, including identity management, encryption, and AI-enabled intrusion detection, are embedded within the cloud infrastructure to ensure resilience against cyber threats. The framework also incorporates intelligent automation workflows that optimize business processes, enforce compliance policies, and support autonomous infrastructure management. By combining AI-driven analytics with secure cloud architectures and regulatory-aware design, the framework enhances operational efficiency, data security, and organizational resilience. The study provides architectural design principles, system integration strategies, and evaluation methods for implementing AI-enabled, cyber-resilient enterprise cloud systems capable of supporting modern financial, healthcare, and regulatory requirements.
Cyber resilient AI driven enterprise cloud framework
regulatory compliance in cloud computing
AI Powered Cyber Resilient Cloud Architecture for Enterprise Systems Financial Platforms Healthcare Analytics and Intelligent Automation
The rapid adoption of cloud computing, artificial intelligence (AI), and digital transformation technologies has significantly reshaped modern enterprise infrastructures. Organizations across sectors such as finance, healthcare, manufacturing, and digital services increasingly depend on cloud platforms to deliver scalable applications, large-scale data processing, and intelligent automation capabilities. However, this technological advancement has simultaneously expanded the cyber threat landscape, exposing organizations to increasingly sophisticated attacks including ransomware, distributed denial-of-service attacks, data breaches, insider threats, and advanced persistent threats. Traditional cybersecurity approaches based on static defenses and reactive monitoring are insufficient to protect complex cloud ecosystems. As a result, the concept of cyber resilience has emerged as a critical strategy to ensure systems can withstand, respond to, and recover from cyber incidents while maintaining operational continuity. This paper proposes an AI-powered cyber resilient cloud architecture designed to enhance security, reliability, and adaptive defense mechanisms for enterprise systems, financial platforms, healthcare analytics infrastructures, and intelligent automation environments. The architecture integrates artificial intelligence-based threat detection, zero-trust security principles, automated incident response, and self-healing infrastructure mechanisms to strengthen resilience against evolving cyber threats. By leveraging AI-driven analytics, behavioral monitoring, and predictive security intelligence, the proposed framework improves real-time threat detection and automated mitigation capabilities. The architecture also supports secure data management, regulatory compliance, and operational efficiency across sensitive domains such as financial services and healthcare. The results demonstrate that integrating AI technologies with cyber resilience principles can significantly enhance security posture, reduce downtime, and enable intelligent autonomous protection within modern cloud infrastructures.
The End of Generative AI Experiments Designing Production‑Grade Data Architectures for LLM Systems
The paper explains the causes of AI pilots’ failures, which are often due to flaws in the model or volatile data. Using a quantitative experimental design, the paper compares and contrasts the big parameter models and the specific models with and without Retrieval Augmentation Generation (RAG). The results showed that data volatility, delay in indexing, and low ingestion pipelines were the primary sources of significant performance loss. AI pilots that operate in Generative AI worked well in the controlled tests, but when they were to perform in the actual production setting, several of them collapsed. The smaller models with strong pipelines were beneficial because of their cost-adjusted value. Risks are governance and security, which began escalating as systems were introduced in the production environments.
Intelligent AI Driven Cloud Native Security Framework for Enterprise Systems Financial Platforms IoT Networks and Real Time Threat Detection
The increasing complexity and interconnectivity of enterprise systems, financial platforms, and IoT networks have intensified cybersecurity challenges, making traditional security mechanisms inadequate. This research proposes an intelligent AI-driven cloud-native security framework designed to provide proactive, adaptive, and real-time threat detection for modern digital ecosystems. Leveraging artificial intelligence and machine learning algorithms, the framework analyzes large-scale network traffic, transaction data, and IoT device activity to detect anomalies and potential security breaches. Cloud-native technologies, including containerization, microservices, and orchestration platforms, enhance scalability, flexibility, and operational resilience, while zero-trust principles ensure robust access control across all system components. The framework integrates intelligent data governance to enforce regulatory compliance, data privacy, and secure information exchange. By combining real-time threat intelligence, automated response mechanisms, and continuous monitoring, the proposed architecture enhances enterprise resilience against cyberattacks, insider threats, and distributed denial-of-service attacks. The framework also supports adaptive security policies that evolve with emerging threat landscapes, enabling organizations to maintain high levels of operational continuity, system integrity, and customer trust. This study highlights the strategic role of AI-driven security frameworks in modern enterprises, offering a holistic approach to secure digital transformation for financial institutions and IoT-dependent infrastructures.
AI Enhanced BI for Dynamic Portfolio Optimization under Market Volatility
Volatile markets make it hard to invest because the values keep fluctuating and there is uncertainty in the market. This is because strategic investment principles may not be suitable to make high-speed movements that may bring low returns or increase risk. BI integrated with Artificial Intelligence (AI) has the benefit of real-time analysis and dynamically-designed decision tools that provide constant portfolio fine-tuning. This is an ability offered by AI that involves analyzing high levels of data, detecting market trends, and controlling assets to keep the portfolio on course towards its goal amidst fluctuation. This integration makes dynamic rebalancing possible such that the portfolio is realigned back to real market terms to reduce risk and maximize return. With these technologies, portfolio managers have an easy time coping with risks in the market and offering better investment propositions that are dynamic and technology-reliant. Combining artificial Intelligence and Business Intelligence is an efficient way to maximize portfolio management in the modern and uncertain financial environment.
Observability in Microservices: From Traditional Monitoring to Distributed System Intelligence
The adoption of microservices structures has transformed the world of software development with unmatched scalability and responsiveness through which monolithic software can be broken down to separate units of manageable software. This change, however, has also added complexities of operations that can best be accommodated by traditional monitoring tools whose design is mainly done in good, host-based settings. This paper discusses how the reactive monitoring can be transformed into proactive and intelligent observability. We compile the findings of 30 current research papers to analyze the shortcomings of legacy systems, the principles of observability (logs, metrics, and traces) and the application of the artificial intelligence to handle high-cardinality telemetry data. The paper draws out the importance of distributed tracing and service mesh telemetry because they have led to the need to have visibility of the inter-service dependencies. We suggest an Observability Maturity Model, which helps organizations to overcome simple infrastructure monitoring to self-healing and predictive systems. The paper argues the role of advanced observability in determining the minimization of Mean Time to Recovery (MTTR) and enhancing the reliability of the system through different case studies.
Face Recognition Door Lock System with IoT &AI
This project presents an AI-Based Smart Door Unlock System using Face Recognition and IoT Integration, which provides a secure and automated access control solution. The system captures facial images using a camera, processes them through machine learning and computer vision techniques, and compares them with a trained image database of authorized users. If a valid match is found, access is granted; otherwise, access is denied and theattempt is logged for security monitoring. The system is designed as a hybrid model that works as a complete software-based authentication platform while also supporting future integration with IoT hardware such as Arduino or Raspberry Pi for physical door unlocking. This scalable architecture ensures flexibility for both academic implementation and real-world deployment. The proposed system enhances security, automation, and reliability while improving user convenience through contactless authentication. It eliminates the need for physical keys, cards, or passwords and reduces the risk of unauthorized access. In addition to authentication, the system supports user management, logging, and monitoring features, making it suitable for smart homes, offices, institutions, and secure facilities. By integrating AI, machine learning, and intelligent system design, the project demonstrates the practical application of advanced technologies in modern security systems. The hybrid and scalable nature of the system makes it a strong foundation for future enhancements such as multi-factor authentication, cloud integration, and large-scale smart security infrastructure deployment. Security and access control have become critical challenges in modern digital and physical environments due to the increasing adoption of smart infrastructure and connected systems. Traditional door locking mechanisms such as mechanical keys, password-based locks, and card-based access systems suffer from limitations including key duplication, unauthorized access, loss of credentials, and lack of intelligent authentication. These conventional systems rely heavily on physical objects or memorized information, making them vulnerable to misuse and human error. With recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision, biometric authentication systems have emerged as a reliable and intelligent alternative. Among these, face recognition stands out as a contactless, user-friendly, and secure authentication method suitable for real-world
D.Naresh, P.Anand, M.Harish, A.Vamshi, A.Kethan
Online Election System to Avoid Fraud Voting by Using Cybersecurity Techniques with the Help of ML Techniques
Online election systems are becoming increasingly popular because they are fast and convenient; however, they are magnets for fraud and hacking. Bad actors look for weak spots in authentication, APIs, and user interfaces to tamper with votes, steal identities, or just try to disrupt the entire process. Old-school security, such as fixed rule-based systems, cannot keep up with the new tricks that fraudsters use. This is where machine learning plays a role. With ML, role fraud can be detected as it occurs, new threats can be predicted, and defenses can be adapted in real time. This study explores how machine learning can make online elections safer and more transparent in the future. We examine how these systems combat identity theft, prevent duplicate voting, and withstand major cyberattacks in the following sections. We propose to build an online election platform that combines tough cybersecurity protocols with smart ML models to keep fraud out and trust in.
Magatam Yogesh Vishwanath, Konna Ganapathi, Korikana Devi Krupa, Kotha Laxmi Naga Bharat Kumar, Kotha Susruth Reddy