American International Journal of Computer Science and Technology
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Reliable Message Delivery in Distributed Edge-Cloud Systems: A Comprehensive Survey
The reliable delivery of messages from edge devices to distributed cloud infrastructure is a foundational requirement for modern industrial, automotive, and consumer IoT ap- plications. Edge environments are characterized by intermittent connectivity, bandwidth constraints, and resource limitations, which make standard reliable transport protocols like TCP insufficient for application-level data integrity. This paper presents a comprehensive survey of the protocols, architectural patterns, and state-of-the-art mechanisms used to ensure reliable message delivery in distributed edge-cloud systems. I analyze key protocols including MQTT, AMQP, CoAP, and QUIC, evaluating their reliability mechanisms such as Quality of Service (QoS) levels, persistent sessions, and stream multiplexing. Furthermore, I survey architectural patterns such as Store-and-Forward, application-level acknowledg-ments, and idempotency mechanisms that operate above the transport layer. Through a comparative analysis, I identify the trade-offs between throughput, latency, and delivery guarantees, providing a decision framework for system architects. Finally, I discuss emerg-ing trends in AI-driven connectivity management and decentralized consensus for message integrity.
Reliable Data Collection in IoT Sensor Networks using Intelligent Proxy Architecture
Internet of Things (IoT) deployments face critical reliability challenges in production environments, where message loss often goes undetected until significant data gaps emerge. Traditional monitoring approaches fail to provide real-time visibility into message loss patterns, making root cause analysis difficult in networks with intermittent connectivity. This paper presents a comprehensive survey of reliability mechanisms for IoT sensor networks and proposes IoTProxyGuard, an intelligent proxy architecture that guarantees message delivery while providing forensic capabilities for debugging production issues. I introduce novel techniques for message loss detection, gap analysis, and automated alerting. My framework combines sequence-based tracking, time-window verification, and statistical anomaly detection to identify missing messages in real-time. Through evaluation of production IoT deployments spanning industrial sensors, environmental monitoring, and smart city infrastructure, I demonstrate that IoTProxyGuard reduces undetected message loss by 99.7% while adding minimal overhead (< 5% latency, < 2% bandwidth). The proxy architecture supports multiple transport protocols and provides detailed forensics for debugging connectivity issues, making it suitable for heterogeneous IoT deployments.
A Scalable Microservices-Based Architecture for AI-Driven Salesforce Cloud Applications
The fast development of enterprise cloud platforms has led to the shift of the paradigm on intelligent, modular and highly scalable digital ecosystems. Salesforce Cloud is now a leading enterprise customer relationship management (CRM) and cloud platform, being constantly enhanced with artificial intelligence (AI) applications like Salesforce Einstein, predictive analytics, and intelligent automation. Traditional monolithic integration strategies, however, are restrictive to the scalability, flexibility and extensibility needed in modern AI-driven enterprise applications. The proposed paper introduces a microservices based scaled architecture of AI-driven Salesforce Cloud applications that can be utilized in making intelligent decisions, real-time analytics, and to achieve elastic scalability and smooth integration with heterogeneous enterprise systems. The architecture is presented as the breakdown of application functionalities into independent services that are loosely coupled to each other and coordinated with help of Kubernetes and connected by API gateways and event-based messaging platforms. The AI workloads are separated into inference and training services, which are specialized to facilitate efficient deployment of the models and manage lifecycle. The paper proposes a layer architecture that includes presentation, orchestration, business services, AI services, data services, and infrastructure layers. All the layers are fault-tolerant, observable, secure, and compliant. It suggests a hybrid deployment model that will make use of Salesforce Platform Events, REST APIs, MuleSoft, and containerized AI pipelines. Performance assessment shows that performance is better in latency of response, throughput, and fault isolation than in monolithic and conventional SOA architecture. The architecture embraces horizontal scaling, multi-cloud implementation and perpetual delivery pipelines, which is appropriate to enterprise-level digital transformation efforts. The findings suggest that AI architectures built with microservices can significantly contribute to the operational agility and reliability as well as intelligence of business applications built on Salesforce. The framework suggested will act as a guide to companies that want to develop next-generation smart CRM, sales automation, customer analytics and service orchestration solutions.
A Hybrid Tourism Recommendation System with Modified Similarity and Weighted Popularity Scores
Obtaining valuable and accurate tourism information can become an overwhelming and time-consuming task to tackle given the vast pool of options available to the consumer. Having a plethora of options with no clear guidelines on how to manage and narrow down choices presents a challenge that can be unwanted for many looking to plan trips and activities in the future. Tourists often spend hours researching different destinations and may still not find the perfect match. Creating a streamlined option to eliminate these issues is, therefore, a worthwhile endeavor. A tourism recommendation system provides a solution by providing tourists with personalized recommendations that are tailored to their specific needs. Recommendation systems, or recommender systems, are a class of artificial intelligence and big data designed to suggest items to users based on prior opinions and preferences, product engagement, and interactions. This can save tourists time and money, and it can help them to have a more enjoyable and memorable travel experience. In this work, we develop a recommender system for Ghanaian tourism websites to provide personalized recommendations to users based on their travel preferences, behavior, and tastes. A combination of collaborative filtering and content-based filtering algorithms have been utilized for this work. This hybrid approach combines the advantages of both methods to create an improved recommendation system. The results obtained ascertain the efficiency of our proposed method.
Robust Object Detection under Extreme Weather Using Physics-Aware Deep Learning
Weaknesses in object detection under extreme weather conditions are an important problem in the current computer vision, especially in applications in autonomous driving, surveillance, and remote sensing. Traditional deep-learning methods like CNNs, YOLO, SSD and Faster R-CNN have been shown to drastically decrease the performance under low-light, fog, rain, snow, and low-contrast environments as a result of occlusion, noise and low-contrast. Physics-aware deep learning combines environmental and physical priors such as atmospheric scattering, rain streak formation and low-light noise models into a neural network to increase feature visibility and enhance detection robustness. In this review, the author thoroughly examines the challenges related to extreme weather, compares the traditional and hybrid physics-informed approaches, and also addresses such architectures as CNN-based, Transformer-based, and multi-modal networks. Surveys are made on datasets, metrics of evaluation, and real-world applications, and the advantages of physics-aware to safety-critical systems are discussed. Lastly, the future research directions and open issues such as multi-modal fusion, self-supervised learning, edge deployment, and secure AI integration are given to inform the creation of dependable object detection systems in unfavorable environmental factors.
Scalable CI/CD Architecture Using Multi-Fleet Controllers and HAProxy for Cluster Management in Kubernetes
Modernizing large-scale enterprise systems presents significant challenges due to tightly coupled legacy architectures, complex deployment dependencies, and high operational risks. The transition from monolithic applications to cloud-native microservices orchestrated by Kubernetes further amplifies deployment and scalability concerns, particularly in environments managing thousands of clusters. Traditional Kubernetes management platforms face inherent scalability limitations, creating bottlenecks in deployment governance, observability, and reliability. This paper presents a scalable continuous integration and continuous deployment (CI/CD) architecture leveraging multiple standalone Fleet controllers integrated with HAProxy for centralized routing and cluster management. By adopting a GitOps-driven deployment model using Fleet, combined with deterministic cluster-to-controller assignment and label-based rollout strategies, the proposed architecture efficiently manages over 10,000 Kubernetes clusters. HAProxy enables seamless access to distributed Fleet controllers through path-based routing, significantly reducing operational complexity and infrastructure overhead.The architecture incorporates automated fleet agent registration using SUSE Manager (MLM), eliminating manual intervention and ensuring balanced controller utilization. Performance optimizations—including etcd quota tuning and HAProxy connection scaling—address real-world operational constraints encountered during large-scale deployments. Experimental results demonstrate improved scalability, reduced deployment latency, and enhanced reliability. This work provides a practical reference architecture for enterprises seeking to implement resilient, large-scale Kubernetes CI/CD systems.
Reinforcement Learning for Optimizing Investment Portfolios in Robo-Advisory Platforms
Artificial Intelligence has quickly revolutionized fintech, but perhaps most notably in automated investment management. Automated advisory services that use algorithms for financial planning while reducing or eliminating human intervention have gained popularity over the years amongst both retail and institutional investors. But conventional approaches to portfolio construction like Modern Portfolio Theory MPT fail to adjust for a rapidly changing world with a flux of constantly changing investor needs. To resolve the aforementioned limitations, this paper studies machine learning, specifically reinforcement learning, which are “teach” the agent to find the best possible actions by utilizing the environment the agent interacts with in order to improve portfolio optimization on robo-advisors. We consider the investment process as a problem of sequential decisions, in which an RL agent allocates the assets at different points in time in order to improve returns and control the risk. Utilizing cutting edge RL algorithms like DQN and PPO, we show in simulations that RL models can achieve superior risk adjusted returns compared to traditional models while also being more adept at handling periods of market volatility. The results point to the applicability of reinforcement learning in developing intelligent, adaptive and personalized robo-advisors as a new paradigm of AI in finance.
Micro-Batch Financial Data Aggregation: Leveraging Throttling for Scalable and Reliable Pipelines
Micro-batch processing has emerged as a pragmatic middle ground between monolithic batch ETL and true record-at-a-time streaming, offering predictable throughput, simplified semantics and easy integration with batch-oriented sinks. In financial systems, where high-volume market feeds, transaction logs and customer event streams coexist with strict consistency, latency and compliance requirements; micro-batching combined with intelligent throttling (rate limiting and backpressure strategies) provides an effective approach to build scalable, resilient and cost-efficient aggregation pipelines. This paper reviews core concepts of micro-batching and throttling, examines architectural patterns and trade-offs important to financial data aggregation and presents design recommendations, operational controls and evaluation metrics. We also discuss integration with modern streaming platforms and highlight practical techniques (adaptive throttling, prioritized queues, idempotent sinks and checkpointing) that together deliver reliable, exactly-once or strongly consistent aggregation with predictable resource usage
Machine Learning Lead Scoring In B2b Hospitality Sales
Understanding B2B sales is an important level when it comes to getting the best outcome from the lead scoring which is a tricky process if not done right; it can be a huge problem for businesses to handle and do it in the right way. Using lead scoring can be a huge process requiring so much labor-intensive work but with technology, everything is working in the right way in terms of classification of data and also analyzing the same data to make sure that it fits what is expected in the long run. Lead scoring is a huge benefit in terms of improving all the rank perspective on which client can be a repeat client in boosting customers and clients with improving leads and sales that are done to fit the prioritized efforts as expected. Having leads doesn’t mean repeat sales or even improving more customers coming in the future, it always starts with having valuable clients that can come many times from the lead generated by marketing teams to make sure that everything works in making sure that business gets more sales which is the ultimate goal of marketing lead scoring. Lead scoring is the key part in making sure that marketing is done successfully with streamlining and improving team sales and marketing efforts
Lakehouse-Integrated Graph Risk Scoring Architectures for Advanced Fraud Detection
The high rate of online transactions, real-time payment systems and interconnected financial ecosystems have, therefore, made fraud detection one of the most serious analytical issues faced in contemporary digital economies. Old methods of fraud detection which were mostly dependent on rule engines and isolated machine learning classifiers cannot rule out scale and velocity of fraud and more importantly the relationship complexity that are involved in modern day fraud trends. The frauds are reported to have started to appear as organized actions within multiple networks of users, devices, accounts, merchants, and transactions and no longer a single record analysis is enough.Simultaneously, lakehouse platforms are changing the nature of enterprise data architectures by integrating data lake scalability with the affordability with the governance, dependability and data warehouse level performance guarantees. Whereas lakehouses are well suited to handling a large sized structured and semi-structured data, they are not necessarily optimized to support graph-based reasoning that is critical in the detection of collusive and network-based fraud. This is the gap that has led to the combination of graph analytics and lakehouse architectures allowing scalable, interpretable and real-time fraud intelligence.In this paper, it is suggested to come up with Lakehouse-Integrated Graph Risk Scoring Architecture (LIGRSA) as a sophisticated way of detecting fraud. The proposed architecture will unify transactional information used in a lakehouse with dynamic graph construction, graph features extraction, and hybrid risk scoring models which are graph metrics combined with machine learning and statistical inference. The architecture allows batch and streaming fraud detection pipelines, which allows propagation of risks between entity networks in near-real time. Graph based risk aggregation and composite fraud scoring mathematical formulations are introduced, scalability, system level design, governance and explainability considerations.The experimental findings, such as representative workloads of financial transactions, prove that lakehouse-integrated graph risk scoring outperforms other models in terms of fraud detection accuracy, early fraud detection, and false positive reduction. In the conclusion, the paper addresses elements of deployment, constraints and future research opportunities, such as real-time graph learning, privacy-preserving analytics, and large language model (LLM)-aided fraud investigation
Leveraging Generative AI in ERP Systems: Use Cases for Higher Education and Public Sector Operations
Generative Artificial Intelligence (GenAI) has emerged as a transformative technology capable of reshaping enterprise resource planning (ERP) ecosystems across industries. Within higher education and the public sector domains characterized by complex administrative workflows, large data volumes, and stringent regulatory requirements GenAI offers unprecedented opportunities to enhance decision-making, streamline operations, and improve stakeholder experience. This paper investigates the integration of GenAI into ERP platforms, presenting architectural frameworks, operational methodologies, and applied use cases. The study provides a detailed exploration of GenAI–ERP synergies, including intelligent process automation, smart procurement, predictive budgeting, automated student services, workforce optimization, and regulatory compliance management. Using qualitative analyses, workflow modeling, and simulated performance assessments, the research highlights improved operational efficiency, reduced manual effort, and enhanced data-driven governance. The proposed methodology includes a multi-layer GenAI-ERP integration model encompassing data pipelines, LLM-based knowledge layers, prompt engineering structures, and adaptive automation workflows. Results from simulated use-case implementations demonstrate measurable improvements: up to 46% reduction in processing time for administrative tasks, 32% increase in forecasting accuracy, and 58% reduction in redundant manual data entry. The paper concludes that GenAI-enhanced ERP ecosystems can deliver strategic value to higher-education institutions and public agencies by enabling personalized services, real-time insights, and operational agility. Future research directions include federated LLM architectures, cross-institutional knowledge graphs, and secure on-prem GenAI deployment models for government-grade environments
Quantum-Enhanced Optimization Models for Large-Scale Security Policy Evaluation in Distributed Cloud-Native Systems
Cloud-native systems, microservice based, container orchestration systems, service meshes, serverless functions, API-gateways and more, produce huge, heterogeneous, and ever-changing security practices. Assessing these policies as being correct, compliant, resolution of conflicts and how best they are enforced has turned out to be a computational intractable problem within classical optimization paradigms with multiple interactions between policies of exponential policy interaction spaces and dynamic systemswith states. In this paper, the author introduces an in-depth research on the topic of quantum-enhanced optimization models in large-scale security policies analysis in distributed cloud-native. Our proposed quantum/classical solution has to do with a hybrid quantum/classical optimization framework that uses quantum annealing and variational quantum algorithms to effectively survey high-dimensional policy configuration spaces without disturbing policy semantics and compliance constraints. The model treats security policies as constraint satisfaction and combinatorial optimal problems and it allows parallel appraisal of contrasting and redundant security policies concerning distributed cloud resources. An elaborate methodology pipeline is proposed, including the process of policy abstraction, modeling as a graph, and strategies of quantum encoding, optimization using strategies, and validation after the process. When compared to classical solvers, scalability, convergence rate and policy conflict detection under high load conditions and multi-cloud configurations have significantly improved. This paper will provide a framework towards the integration of near term quantum computing into cloud security governance and provide a future prospectus to persist in cloud security policy assessment in next generation cloud-native architectures
Multi-Cloud ECM/WCM Orchestration with AI: A Scalable and Intelligent Enterprise Architecture
The exponential growth of enterprise content and the widespread adoption of heterogeneous cloud platforms have introduced significant complexity in managing Enterprise Content Management (ECM) and Web Content Management (WCM) systems. Traditional, monolithic content architectures are increasingly inadequate in addressing scalability, interoperability, governance, and resilience requirements in multi-cloud environments. This paper presents a scalable and intelligent multi-cloud ECM/WCM orchestration architecture powered by Artificial Intelligence (AI), designed to enable adaptive content lifecycle management, intelligent workflow automation, and consistent governance across distributed cloud infrastructures. The proposed architecture adopts cloud-native and event-driven principles, decoupling content services from underlying infrastructure to support elasticity, fault tolerance, and vendor independence. AI models embedded within the orchestration layer provide automated content classification, context-aware metadata enrichment, predictive lifecycle management, and dynamic workflow optimization. These capabilities enable the system to respond proactively to changing workloads, user behavior, and regulatory constraints. Security, privacy, and compliance are addressed through integrated identity management, encryption, continuous monitoring, and policy-aware orchestration. Performance evaluation in hybrid multi-cloud environments demonstrates notable improvements in availability, infrastructure utilization, deployment efficiency, and operational cost reduction when compared to traditional ECM/WCM platforms. The results highlight the effectiveness of AI-driven orchestration in enhancing system resilience, optimizing resource usage, and supporting large-scale enterprise content operations. This work contributes a comprehensive reference architecture suitable for modern digital enterprises seeking intelligent, cloud-agnostic ECM/WCM solutions aligned with evolving business and regulatory demands
Private LLMs for Higher Education: Secure GenAI for Academic & Administrative Content
Generative Artificial Intelligence (GenAI) based on Large Language Models (LLMs) has rapidly transformed knowledge-intensive domains, including higher education. Universities and academic institutions increasingly rely on LLMs for academic writing support, intelligent tutoring systems, admissions processing, research assistance, and administrative automation. However, the adoption of public, cloud-hosted LLMs introduces critical risks related to data privacy, intellectual property leakage, regulatory non-compliance, and institutional sovereignty. Sensitive academic data such as student records, examination materials, unpublished research, and administrative communications cannot be safely exposed to external GenAI platforms governed by opaque data retention and training policies. In response to these challenges, Private LLMs have emerged as a secure and controllable alternative, enabling institutions to deploy GenAI capabilities within on-premise or institution-controlled cloud infrastructures. Private LLMs preserve data confidentiality while offering customization aligned with institutional pedagogy, governance, and compliance requirements. This paper presents a comprehensive study on the design, deployment, and evaluation of Private LLMs tailored for higher education environments. It examines architectural frameworks, security mechanisms, fine-tuning strategies, and governance models that support academic and administrative use cases. The study further analyzes the trade-offs between performance, scalability, and security when compared to public LLM services. Experimental evaluation demonstrates that well-optimized private LLMs can achieve competitive performance while ensuring compliance with data protection regulations such as FERPA, GDPR, and institutional ethical guidelines. The paper concludes that Private LLMs represent a sustainable and secure pathway for the responsible adoption of GenAI in higher education, fostering innovation without compromising trust, privacy, or academic integrity
Streaming Analytics Architectures for Live TV Evaluation and Ad Performance Optimization
Broadcasting on live television (TV) remains one of the most widespread systems of distributing real-time content even in the face of over-the-top (OTT) and on-demand media. In this respect, the incorporation of streaming analytics into live TV operations has been implemented as an urgent need of advertisers who require real-time feedback. In this paper, the architecture, design thinking and algorithms of real-time analytics pipeline are explored with an eye to assess the performance of real-time televised content and optimize advertisements (ad) delivery processes. The work is given a high-velocity data ingestion, stream processing schemes, in-memory calculation, model appraisal, ad-placement cost, occasion correlation, and audience involvement evaluation. They are proposed as a set of architectural designs that involve the use of distributed message queues, stream-processing engines, micro-batch pipelines, real-time machine learning (ML) inference, multi-modal viewer attribution signals, and smart caching. The suggested architecture builds upon the legacy broadcast patterns with scalable analytics layers that are able to process heterogeneous information tracks like viewership, STB (set-top box) telemetry, second-screen interactions, content metadata, ad-impression, and social media signals. The literature review covers existing architectures as well as Lambda, Kappa and hybrid streaming-batch models and comparisons the popular streaming technology implementations Apache Kafka, Flink, Spark streaming and AWS Kinesis. New methodology, which is Adaptive Multi-layer Streaming Analytics Framework (AMLSAF), is presented to optimize ad-performance using dynamic viewer-attention modelling, real-time A/B testing, and predictive ad-fatigue scoring. The experimental findings depict that ad-conversion signals are measurably improved, latency decreases, and viewership estimation improves. The discussion identifies the difficulty of scaling, fault tolerance and guaranteeing sub-second latency in peak events of live communications. At the end of the paper, some thoughts on the development in the future are provided, including edge-based analytics, federated viewer modelling, zero-copy data movement and AI-generated dynamic ad-personalization
Enterprise Data Mesh Architectures for Scalable and Distributed Analytics
Businesses are facing the volcanic increase in data volume, variety and dispersal in hybrid and multi-cloud setup, which is revealing the constraints of the centralized data warehouse, as well as the monolithic data lakes. Data mesh has become a socio-technical framework that devolves data ownership to domain teams with shared standards on interoperability, governance and security. The presented paper offers a reference enterprise data mesh architecture to meet scalable and distributed analytics. The architecture is designed to follow four pillars, namely domain-oriented data products, a self-service data and machine learning platform, standardized interoperability layer, and a federated computational governance model. Are outlining an implementation framework which is event-driven ingestion, API-first data product exposure, unified metadata management, distributed query capabilities and cross-domain orchestration. Based on the recent empirical findings and synthesized benchmarks, Present the discussion on the gains in the time-to-insight, data quality, reusability, and governance effectiveness and on the measures of scalability in the latency and the number of concurrent users. Other challenges such as organizational resistance, tooling fragmentation, and standards gaps are also analyzed in the paper and then the paper outlines future directions in AI-augmented governance and formal, data product specifications. The proposed model is an effective roadmap that enables enterprises to operationalize data mesh and achieve real-time, reliable analytics at scale by aligning organizational design and a strong layered architecture
High-Performance Distributed Database Partitioning Using Machine Learning-Driven Workload Forecasting and Query Optimization
The modern large-scale applications have distributed databases as their core, and it is challenging to keep the latency low and the load balanced during the highly variable workloads. The common way of using static range or hash partitioning and traditional cost-based optimizers is as a one-time tuned system that is then allowed to run under changing access patterns resulting in skew of data and communication across partitions and inconsistent performance. In this paper, a single architecture of the High-Performance Distributed Database Partitioning is suggested whereby the Machine Learning-Fueled Workload Forecasting is integrated with AI-Aided Query Optimization. Workload forecasting module takes in streams of telemetry, query arrival rate, key access frequencies, utilization of resources, and uses the time-series and deep sequence models to forecast short term traffic patterns and the occurrence of hot keys. These forecasts drive an adaptive partitioning engine that selects split, merge, and migration actions to rebalance shards while explicitly constraining data-movement overhead. Simultaneously, a query optimizer based on learning applies learned cost models and cardinality estimators which know the current and predicted partition layout, and use this to compute topology-aware join order, replica choice and operator choice. The two elements constitute the closed feedback loop with the distributed storage layer, constantly improving the models as per the observed performance. Benchmark and cloud-inspired workload experimental assessments show that it has made great leaps in terms of latency, throughput, and load balance compared to the state-of-the-art learned optimizers, and scaled down to convergence much more rapidly than static partitioning and converges with fewer repartitioning actions. The findings point to the promise of an intensive integration of forecasting and query optimization as a viable step to self-optimizing, workload-conscious distributed databases
Homomorphic Distributed Database Partitioning
Machine Learning–Driven Workload Forecasting
Responsible Machine Learning in Student-Facing Applications: Bias Mitigation & Fairness Frameworks
The increasing deployment of machine learning (ML) in student-facing applications—such as academic performance prediction, automated assessment, intelligent tutoring, and early-warning systems—has amplified concerns about bias, fairness, and accountability in educational decision-making. As of 2024, educational institutions rely heavily on data-driven models to support high-impact student outcomes, making responsible ML practices essential for protecting equity and trust. This paper investigates algorithmic bias in student-facing ML systems and evaluates contemporary fairness frameworks and mitigation strategies across the full ML lifecycle. Using large-scale educational data, the study analyzes sources of bias arising from socioeconomic, demographic, and behavioral factors, and examines their influence on predictive models. The paper reviews group, individual, and counterfactual fairness metrics, highlighting practical trade-offs between fairness and predictive accuracy. A comprehensive responsible ML fairness framework is proposed, integrating privacy preservation, fairness-aware learning, continuous monitoring, and human-in-the-loop governance. Empirical evaluation on the STAAR dataset from Texas public schools (2012–2019, approximately five million students) demonstrates that bias mitigation techniques can improve fairness metrics by over 20% on average, while revealing inherent fairness–accuracy and interpretability trade-offs. The findings emphasize that fairness is not a one-time intervention but a continuous operational requirement. This work provides actionable guidance for designing, deploying, and governing equitable ML systems in educational environments, aligning technical innovation with ethical responsibility and inclusive student outcomes
A Multi-Layered Zero-Trust–Driven Cybersecurity Framework Integrating Deep Learning and Automated Compliance for Heterogeneous Enterprise Clouds
Modern enterprises increasingly operate across heterogeneous computing environments encompassing public cloud platforms (e.g., AWS, Azure, and GCP), private clouds, on-premises data centers, Kubernetes clusters, and edge infrastructures. While this architectural diversity enables scalability and agility, it also significantly expands the attack surface and complicates the enforcement of consistent security and regulatory controls. Traditional perimeter-based security models and static compliance mechanisms are insufficient to address the dynamic, distributed, and identity-centric nature of these environments. This paper proposes a multi-layered Zero Trust–driven cybersecurity framework that unifies identity-centric access control, continuous verification, and adaptive policy enforcement across heterogeneous enterprise clouds. The framework integrates deep learning–based behavioral analytics to enable real-time threat detection, risk scoring, and anomaly identification across users, workloads, and services. In parallel, an automated compliance engine implements compliance-as-code principles, continuously validating security posture against regulatory requirements and dynamically enforcing policies across multi-cloud and containerized environments. The proposed architecture is evaluated using a representative enterprise multi-cloud deployment, incorporating public cloud services, Kubernetes workloads, and on-premises resources. Experimental results demonstrate improved threat detection accuracy, reduced mean time to detection and response, and higher compliance adherence compared with conventional rule-based and perimeter-centric approaches, while maintaining acceptable system overhead. The key contribution of this work lies in delivering a unified, scalable, and intelligent Zero Trust framework that tightly couples deep learning–driven security analytics with automated compliance enforcement, providing a practical and extensible foundation for securing modern heterogeneous enterprise cloud ecosystems
The evolution toward sixth-generation (6G) wireless systems demands a shift from traditionally engineered networks to AI-native network management, where intelligence is embedded across the entire architectural stack. As 6G targets extreme performance metrics sub-millisecond latency, terabit-level throughput, integrated sensing, and massive device density, legacy management approaches become insufficient (Dang et al., 2020; Zhang et al., 2021). Recent studies argue that embedding machine learning, distributed intelligence, and real-time automation into network control is essential for managing future complexity and enabling autonomous operation (Saad et al., 2020; Chen et al., 2022). AI-native management integrates technologies such as multi-agent reinforcement learning, network digital twins, semantic communication, and intent-based orchestration to create self-optimizing, self-healing, and context-aware networks (Flórez et al., 2023; Mahmood et al., 2024). This paper examines the conceptual foundations, architectural principles, and enabling technologies for AI-native 6G network management. It further analyzes emerging challenges—including data sparsity, model explainability, cross-domain trust, and scalability and outlines open research directions critical for achieving fully autonomous, resilient, and adaptive 6G networks. The findings suggest that AI-native design is not an enhancement but a fundamental requirement for realizing the operational ambitions of next-generation communication systems
Traffic Engineering for Massive Data Flows
The exponential growth of data-intensive applications ranging from cloud computing and real-time analytics to IoT and AI-driven workloads has led to unprecedented challenges in managing massive data flows across large-scale network infrastructures. Traditional traffic engineering (TE) mechanisms, designed for static and predictable traffic patterns, are increasingly inadequate in ensuring optimal performance, scalability, and reliability. This research explores modern TE frameworks that integrate software-defined networking (SDN), network function virtualization (NFV), and machine learning (ML) to dynamically optimize traffic flow and resource allocation in high-throughput environments. The paper examines the architectural evolution of TE, advanced optimization algorithms, and adaptive routing techniques capable of responding to real-time network variations. Moreover, it evaluates the trade-offs between centralized and distributed control paradigms in large-scale data centers and backbone networks. By consolidating current advances and identifying persistent challenges, this study provides a comprehensive understanding of next-generation traffic engineering strategies designed to support massive data flows in the era of intelligent and autonomous networksa
Enhancing Market Forecast Accuracy through Ensemble Predictive Modeling and Behavioral Data Integration
This paper proposes an ensemble-based behavioral forecasting framework that extends traditional market prediction methods beyond purely quantitative indicators such as price movements, macroeconomic variables, and historical patterns. The research is inspired by the increasing influence of sentiment-based and behavioral forces within the ever-changing and emotionally charged markets and combines consumer sentiment, search intensity on the internet, and social media presence with macro-level psychological indicators in a single predictive framework. The framework is a multi-layer ensemble of machine learning based on econometric models. Sentiment extraction pipelines, attention-based neural encoders, and normalized behavioral indices are used to transform noisy, unstructured behavioral data into robust predictive signals. Linear econometric models are used to capture long-term trends, tree based learners are used to model nonlinear interactions, and neural networks are used to identify latent behavioral patterns, stacking and model averaging are used to reduce overfitting and enhance generalization. Simulations and experiments on both simulated and actual-world data sets of equities, commodities and retail-demand series show that behavioral-enhanced ensembles are always superior to single-ML and conventional econometric baselines in the short and medium-term predictions. Error-decomposition indicates that the difference in forecast variance decreases with the addition of behavioral features, and forecasting turning points in the market is better by up to 27% during economic uncertainty periods, trending regimes, and event-driven cycles. The explainability analysis using SHAP also indicates that, in most cases, behavioral indicators are among the most important in the ensemble. The paper contributes a scalable methodology encompassing data preprocessing, mixed-type normalization, hyperparameter tuning workflows, and resilient stacking architectures suitable for turbulent market conditions. It offers a rigorous methodological foundation for behavior-driven forecasting in finance, retail analytics, and macroeconomic applications, and outlines future directions including multimodal behavioral data integration and guidelines for ethical, privacy-aware deployment.
Real-Time Predictive Analytics for Continuous Monitoring Of Patient Electronic Health Records
Because of advancements regarding technology & the widespread implementation of Electronic Health Records (EHRs), health care information is growing very exponentially. This means that physicians need to have up-to-date predictive analytics tools to help them make quick decisions. The goal of this project is to build a system that will allow powerful statistical modeling techniques to continuously check client electronic health information. This technique combines machine learning procedures with current data streams from medical information infrastructures. This helps you see problems early, predict major health events & act before they happen. The system combines preparation of data techniques to handle different kinds of clinical data, feature extraction to discover appropriate trends & predictive techniques like recurrent neural networks & algorithms for finding anomalies to look at patterns throughout time. Datasets from available EHR sources were assessed to train & validate the model, demonstrating its accuracy in identifying early signs of patient deterioration, sepsis risk & abnormal vital sign fluctuations. Doctors set up a unified analytical workflow by using Apache Kafka for data streaming, TensorFlow for model training & visualization dashboards. The results show that response speed, diagnostic accuracy & patient safety have all improved a lot compared to traditional batch-based monitoring systems. The recommended approach makes it very less difficult for healthcare to function over actual time & sets the foundation for smart systems for clinical decision-making. Further enhancements might include adding wearable technology, using reinforcement education for adaptive supervision & using privacy-preserving analytics to make sure that healthcare data regulations like HIPAA are followed. This research shows how actual time predictive analytics can turn raw healthcare data into useful information, which may improve patient outcomes & make modern healthcare systems work better
Productionizing GPU Inference on EKS with KServe and NVIDIA Triton
The increasing use of AI-based applications has brought about a necessity for operationalizing GPU inference on a large scale in production environments. But, the deployment and management of GPU-accelerated machine learning models in the wild are still major challenges that arise from the complexity of infrastructure orchestration, cost management, and model lifecycle automation. This paper is an exploration of a complete framework for productionising GPU inference on Amazon Elastic Kubernetes Service (EKS) with the help of KServe and NVIDIA Triton Inference Server, thus providing a simplified route from model deployment to large-scale checkpointed inference. Amazon EKS provides a controlled Kubernetes base that takes care of the automatic scaling, resilience, and security, whereas KServe makes it easy for the models to be served with the help of standardized APIs and native autoscaling for inference workloads. NVIDIA Triton complements this stack by offering the best performance, single or multi-framework, through GPU optimization, dynamic batching, and model ensemble all very important features to the maximum use of the hardware. They all together form a complete pipeline that is compatible with present-day MLOps practices, thus helping the continuous integration, model versioning, and automated rollouts. This paper also talks about the ways of keeping a balance between the performance and the cost such as GPU sharing, autoscaling policies, and efficient pod scheduling. Using this EKS–KServe–Triton trio, organizations can turn experimental ML models into production-grade, scalable inference services, thus closing the gap between model development and real-world deployment with a cloud-native, cost-optimized approach
AI-Driven Data Engineering Workflows for Dynamic ETL Optimization in Cloud-Native Data Analytics Ecosystems
Cloud-native analytics requires ETL pipelines which are continuously able to adjust themselves to changing volumes of data, changeable schema, and narrow cost-performance SLOs. The paper introduces an AI-based workflow that will turn ETL into a self-optimizing, closed-loop system that is not based on heuristics. A metadata-first control plane models datasets, lineage, contracts, and policies; supervised models learn stage-level tactics (partitioning, join ordering, file sizing, compaction cadence) from execution traces; and a reinforcement-learning scheduler allocates resources and reorders DAGs based on live telemetry (throughput, p95 latency, spill rates, drift scores, and marginal cost). Predictive scaling intelligently right-sizes compute in Kubernetes/Spark/Flink/Dataflow, and Anomaly detector contract-sensitive gates bad inputs and instigates target corrective (imputation, schema evolution, selective backfills) corrective actions. It is governed through policy-as-code that has audit logs, row/column controls, and time-travel semantics in open table formats (Delta/Iceberg/Hudi). The methodology reduced data error rates, improved processing speed, and decreased the time of manual maintenance and reduced effective cloud spending in mixed streaming-batch evaluations by spot-aware allocation and throttling of low-value work. The result is a resilient, transparent ETL fabric that aligns engineering actions with business SLOs and budgets, accelerating trustworthy BI and ML delivery in multi-tenant, cloud-native environments
Dinesh Babu Govindarajulunaidu Sambath Narayanan
Semantic Layer Construction in Data Warehouses Using GenAI for Contextualized Analytical Query Processing
Huge proliferation of enterprise data has fueled development of data warehouse (DW) architectures that have the capability of supporting big analytical workloads. But currently with the advent of the business user, they are demanding more and more semantic knowledge and query processing based on context and meaning, which the traditional metadata layer or OLAP cube simply does not offer. It is a study undertaken by proposing a Generative AI (GenAI)-friendly solution to build an intelligent semantic layer that allows the representation of contextual query comprehension, identification of semantic links, and knowledge grounding through the use of organized enterprise data. The proposed system combines Large Language Models (LLM) with semantic enrichment pipelines, which dynamic process business terminologies, reconcile queries with unified vocabularies, and convert them into optimized plans of DW execution. The model employs entity linking, embedding based schema matching, construction of knowledge graphs and contextual query rewrites to provide superior analytical accuracy. Furthermore, there is the introduction of a hybrid vector + relational index model that provides a better performance on natural-language query analysis. Complete comparisons on range of scenarios involving enterprise scale benchmark databases show substantial performance enhancements - an average accuracy of 38.6 in business query interpretation and 31.2 in latency reduction of semantic-driven workloads, compared to the traditional keyword-based systems. The architecture enables business glossaries to be adaptive, governance policy-enabled, and human-data collaboration, which is a very strong base on the topic of next-generation analytical ecosystems and enterprise decision intelligence. This paper includes: (1) An extensive literature review of semantic data warehousing, and AI based contextual analytics. (2) An elaborate procedure of GenAI-assisted weighted layer of semantics. (3) Usability, accuracy and performance experimental evaluations. (4) Major lessons and directions of future research on autonomous semantic data management. The results validate that GenAI technologies have the ability to turn data warehouses into smart analytical systems that are able to comprehend business intent, in turn, facilitating the rapidity of insights, decreasing the manual modeling task, and fostering the enterprise-wide democratisation of data among users
Implementing Real-Time ADT Event Processing for Case Management Triggering in Medicaid Populations
Timely and proactive care coordination is essential for improving outcomes in Medicaid populations, who often experience high rates of chronic illness, care fragmentation, and social vulnerability. Admissions, discharges, and transfers (ADT) represent key care transition events that, if detected and acted upon in real-time, can trigger meaningful interventions by case management teams. This paper presents the design, implementation, and evaluation of a scalable, standards-based architecture for real-time ADT event processing, specifically tailored for Medicaid case management workflows. Our system ingests HL7 v2.x ADT messages via Apache Kafka, transforms them into enriched FHIR resources, and applies configurable trigger logic to determine actionable care events. A patient attribution layer and rule engine identify high-risk scenarios such as preventable discharges or repeat ED visits. We validate our pipeline using 50,000 synthetically generated ADT messages representing Medicaid-like populations. Key metrics—including end-to-end latency, trigger precision, and case initiation rates—are analyzed to assess system performance. The results demonstrate that real-time ADT processing significantly improves responsiveness, enabling same-day outreach and reducing missed interventions. This approach lays the groundwork for intelligent, event-driven care coordination under value-based care models
Scaling Telemedicine Platforms with Cloud-Native DevOps: An Architecture for Reliable Patient Services
Telemedicine applications depend on uninterrupted patient access, rapid feature updates, and fault-tolerant data handling across distributed environments. Traditional monolithic deployments struggle to provide elastic scalability, real-time reliability, and operational resilience during unpredictable spikes in remote healthcare demands. This paper proposes a cloud-native DevOps architecture that combines micro services, container workloads, and Kubernetes orchestration to ensure reliable telemedicine service delivery. The approach aligns infrastructure automation, continuous delivery pipelines, and auto scaling policies to support dynamic patient traffic, resilient service communication, and secure data flows across modular components. Reliability is achieved by combining horizontal scaling and service mesh routing with health-checking, rollback mechanisms, and distributed monitoring that treat patient-facing services as independently deployable units. The resulting design supports dependable consultations, responsive clinical workflows, and stable telehealth performance without interrupting active patient sessions. By embracing DevOps automation and micro service decomposition, telemedicine systems can evolve continuously while maintaining consistent service quality under demanding operational conditions
Modernizing 834 Outbound Processing: Leveraging Incremental ETL Frameworks to Improve Health Plan Data Exchanges
834 EDI files are critical to the administration of health insurance enrollments. Traditional outbound processing methods are batch-based and often fail to deliver the real-time or near-real-time responsiveness needed by modern healthcare applications. This paper introduces an incremental extract, transform, and load (ETL) framework designed to optimize the outbound flow of 834 data. We present a scalable architecture, analyze performance improvements across latency, accuracy, and cost, and demonstrate applicability through real-world datasets from major health plans. Results show a 65% improvement in processing latency and a 30% cost reduction over batch-based models.
Copyright (c) 2021 American International Journal of Computer Science and Technology
Cloud Orchestration with Kubernetes/Docker
The increasing adoption of artificial intelligence (AI) workloads has placed significant demands on cloud-native infrastructure, particularly in terms of scalability, resource isolation, and automated management of containerised services. Container orchestration platforms such as Kubernetes and Docker have thereby become critical enablers for deploying AI/ML pipelines at scale. For example, research shows that Kubernetes is effective for container orchestration in AI cloud environments (Lokiny, 2022). Additionally, machine-learning–based orchestration frameworks for containers have been explored to improve scheduling, allocation and performance (Zhong et al., 2021). Yet, despite these advances, there remains a paucity of comparative analysis focused on AI workloads especially those that contrast orchestration platforms in hybrid or multi-cloud settings, and that evaluate metrics such as latency, throughput, fault-tolerance, and cost-efficiency. This paper presents a systematic comparative study of Docker- and Kubernetes-based orchestration frameworks for AI workloads, employing a multi-factor benchmark across scalability, resource utilisation, fault resilience, and operational cost. The experimental setup utilises micro-service and deep-learning inference pipelines deployed via Docker Swarm and Kubernetes across public cloud infrastructure. Results indicate that Kubernetes outperforms Docker Swarm in horizontal scaling and fault resilience, while Docker Swarm demonstrates marginal benefits in simplicity of deployment and lower management overhead in small-scale scenarios. Furthermore, the cost-performance trade-offs reveal that orchestration maturity and autoscaling policies favour Kubernetes when workloads grow beyond moderate scale. The paper discusses the implications for AI DevOps teams and cloud architects, offering guidelines for selecting and configuring orchestration technologies aligned with AI workload characteristics. In conclusion, as AI workloads continue shifting toward containerised, distributed, and hybrid-cloud environments, the orchestration strategy plays a pivotal role in ensuring performance, reliability, and cost-efficiency of the underlying infrastructure