International Journal of Engineering & Extended Technologies Research (IJEETR)
Welcome to IJEETR The International Journal of Engineering & Extended Technologies Research (IJEETR) is a prestigious, peer-reviewed, open-access international journal committed to fostering innovation, collaboration, and excellence in research across the broad spectrum of engineering and technological sciences. In a rapidly evolving world where technology is reshaping every facet of human life, IJEETR strives to be more than just a journal – it is a knowledge hub where scholars, academicians, researchers, and industry practitioners come together to exchange pioneering ideas, transformative research, and ground breaking solutions to global challenges. We believe in bridging the gap between theory and practice, encouraging interdisciplinary collaboration, and ensuring that innovative discoveries reach a worldwide audience with maximum impact. Our Vision & Mission · Vision: To be recognized globally as a leading platform for the dissemination of high-quality, innovative, and impactful research in engineering and extended technologies. · Mission: o To promote a culture of scientific excellence and innovation. o To provide open access to research, ensuring equitable knowledge sharing. o To accelerate the application of research findings into real-world practices. o To foster collaboration between academia, industry, and society.
Secure Next-Gen SAP HANA Cloud: GenAI, Real-Time Staffing Automation, Healthcare Data Integrity, and ML-Powered Fraud Detection with MFA
The evolution of enterprise cloud platforms is accelerating through the integration of generative AI, machine learning, and advanced cybersecurity frameworks. Secure Next-Gen SAP HANA Cloud presents a unified architecture that combines GenAI-driven insights, real-time staffing automation, and healthcare-grade data integrity controls to enhance operational efficiency and regulatory compliance. Leveraging SAP HANA’s in-memory performance, the solution embeds ML-powered fraud detection strengthened with multi-factor authentication (MFA) to counter identity-centric attacks and ensure secure, trusted access. By automating workforce optimization, improving clinical and operational data quality, and delivering continuous intelligence at scale, this next-generation platform empowers organizations to modernize securely. The result is a resilient, intelligent, and cyber-hardened cloud ecosystem capable of supporting high-stakes workloads across industries.
AI-Augmented Gray Relational Modeling for Multi-Tenant SAP HANA Clouds: Petabyte-Scale Apache Processing for Fraud Detection, Adaptive Risk Intelligence, and Cybersecurity
The rapid expansion of multi-tenant SAP HANA Cloud environments has intensified the need for scalable, intelligent, and secure analytics frameworks. This paper introduces an AI-augmented Gray Relational Modeling (AI-GRM) approach designed to analyze complex, high-dimensional behavioral data across petabyte-scale Apache processing pipelines. The proposed architecture integrates deep neural embeddings with classical GRM to enhance pattern discrimination, anomaly sensitivity, and cross-tenant relational weighting. Leveraging real-time stream ingestion and distributed in-memory computing, the system supports high-throughput fraud detection, adaptive risk intelligence, and continuous cybersecurity monitoring. Experimental evaluations with synthetic and enterprise-scale logs indicate that AI-GRM improves detection precision and relational clarity under sparse, noisy, and multidomain data conditions. The framework demonstrates strong adaptability for regulatory compliance, cyber-threat forensics, and enterprise-grade operational resilience. Findings highlight the potential of hybrid gray-system intelligence and machine learning to advance next-generation cloud security strategies.
Securing Multi-Cloud Architectures with AI-Enabled Fraud Detection: Causal Trace Miner Analytics and ERP-Integrated Fraud Prevention Techniques
The proliferation of multi-cloud environments has increased the complexity of managing security and detecting fraud across enterprise systems. This study proposes an AI-enabled framework that integrates causal trace miner analytics with ERP systems to enhance fraud detection and prevention. By leveraging advanced machine learning models, multivariate threat classification, and real-time monitoring, the framework identifies fraudulent activities in financial transactions with high accuracy. The ERP integration allows seamless coordination between operational and security data, providing a holistic approach to risk management. Experimental results demonstrate significant improvements in fraud detection rates and reduced response times, highlighting the potential of AI and ERP-based approaches in securing multi-cloud infrastructures.
AI-Driven Network Security for Cloud Systems: Addressing AI Integration Challenges with Multi-Factor Authentication, Multivariate Classification, and Semantic Precedent Retrieval
The rapid transition to cloud-based infrastructures has intensified the need for intelligent, adaptive, and robust security mechanisms capable of responding to complex cyber threats. This paper proposes an AI-driven network security framework for cloud systems that addresses critical challenges associated with integrating artificial intelligence into security workflows. The architecture employs multi-factor authentication (MFA) to strengthen identity verification and reduce unauthorized access across distributed cloud environments. Multivariate classification models enhance threat detection precision by analyzing multidimensional behavioral, transactional, and network features to identify anomalous or malicious activity. A novel Semantic Precedent Retrieval component augments decision-making by referencing historical threat patterns, security incidents, and contextual metadata to improve risk scoring and policy enforcement. The fusion of these capabilities creates a unified, adaptive defense layer that supports real-time monitoring, automated remediation, and improved situational awareness. Experimental evaluation demonstrates reduced false positives, enhanced model interpretability, and improved resilience against advanced cloud-native attacks. The proposed framework provides a scalable, intelligent foundation for securing next-generation cloud networks.
A Scalable Distributed Cloud–AI Defense Framework for Financial Networks Multivariate Threat Analysis, DevSecOps Security Automation, and SAP ERP–Based Fraud Detection
Financial networks face increasingly complex cyber threats driven by high-volume transactions, interconnected systems, and sophisticated fraud mechanisms. This paper introduces a scalable distributed Cloud–AI defense framework designed to enhance cybersecurity resilience and fraud detection across financial ecosystems. The proposed architecture integrates multivariate threat analysis, leveraging deep learning and anomaly detection models to identify advanced attack vectors, abnormal transaction behaviors, and fraud patterns in real time. A DevSecOps-driven security automation layer enables continuous integration and continuous delivery (CI/CD) hardening, automated vulnerability remediation, and policy enforcement across cloud-native environments. Additionally, SAP ERP–based fraud detection modules support real-time transactional analytics, cross-ledger validation, and behavioral scoring to uncover hidden fraud patterns within enterprise operations. The distributed cloud foundation ensures scalability, high availability, and secure data exchange across financial institutions. Experimental evaluation demonstrates that the framework significantly improves detection accuracy, reduces incident response time, and strengthens overall risk posture, offering an end-to-end AI-enhanced defense ecosystem for modern financial networks.
AI-Enabled Cloud Architecture for Banking ERP Systems with Intelligent Data Storage and Automation using SAP
The increasing digital transformation across the banking sector requires scalable, secure, and intelligent platforms capable of supporting real-time decision-making and automated financial operations. This study presents an AI-enabled cloud architecture for banking ERP systems integrated with SAP to enhance automation, optimize workflows, and ensure secure data management. The proposed framework leverages SAP HANA’s in-memory computing and cloud-native capabilities to accelerate analytics, improve transactional efficiency, and reduce operational latency. Machine learning models are incorporated to support intelligent fraud detection, predictive risk assessment, and process automation. A secure, intelligent storage layer ensures compliance with financial regulations while enabling seamless data retrieval and lifecycle governance. The architecture also incorporates Zero-Trust cybersecurity principles to protect against evolving threats and ensure resilient operations. Overall, the model demonstrates a unified platform that improves scalability, enhances automation, and strengthens digital banking ecosystem performance.
RiskPredict360 AI-Powered Framework for Credit Card Fraud Detection with Deep Neural Networks, Self-Service Analytics, and SAP HANA ERP Cloud Integration
Credit card fraud continues to pose significant challenges for financial institutions, requiring advanced predictive and real-time detection mechanisms. This paper presents RiskPredict360, an AI-powered framework designed to detect and prevent credit card fraud by leveraging deep neural networks, self-service analytics, and SAP HANA ERP cloud integration. The framework utilizes deep learning models to analyze transactional patterns and identify anomalous behaviors indicative of fraud. Self-service analytics empower financial analysts to explore trends and generate actionable insights without extensive technical expertise, while SAP HANA–powered cloud infrastructure ensures high-speed data processing, secure storage, and seamless ERP system integration. RiskPredict360 also supports scalable deployment across enterprise environments, enabling real-time monitoring, risk scoring, and automated alerts to mitigate potential fraud incidents efficiently. Experimental evaluations demonstrate the framework’s effectiveness in improving detection accuracy, reducing false positives, and enhancing operational responsiveness, offering a robust solution for modern financial cybersecurity needs.
A Privacy-Preserving LLM-Integrated DevOps and Testing Framework for ERP-Enabled Rural Healthcare Cloud Platforms with Intelligent Fraud Prevention
Rural healthcare systems increasingly rely on ERP-enabled cloud platforms to streamline operations, improve patient outcomes, and support real-time decision-making. However, these environments face critical challenges related to data privacy, system reliability, and sophisticated fraud attempts targeting financial and clinical workflows. This paper proposes a privacy-preserving DevOps and testing framework integrated with Large Language Models (LLMs) to enhance security, automation, and operational efficiency across rural healthcare infrastructures. The framework leverages secure LLM agents for automated code review, anomaly detection, intelligent test generation, and continuous monitoring while enforcing strict data minimization and encryption protocols to safeguard sensitive patient information. Additionally, the system incorporates AI-driven fraud prevention mechanisms using machine learning, behavioral analytics, and rule-based engines within ERP and cloud layers to detect credit card misuse, insurance manipulation, and identity-based fraud in real time. By combining DevOps automation, privacy-by-design principles, and intelligent threat detection, the proposed solution delivers a scalable, resilient, and secure architecture tailored for resource-constrained rural healthcare environments.
Deep Neural Network–Driven Credit Card Fraud Detection in Cloud Environments: Integrating Self-Serve Analytics, Cybersecurity Best Practices, and Quantum Machine Learning
The rapid expansion of digital payments and cloud-based financial services has increased the complexity and scale of credit card fraud, demanding more intelligent, adaptive, and secure detection mechanisms. This research presents a Deep Neural Network–driven credit card fraud detection framework optimized for cloud environments, designed to enhance scalability, latency performance, and real-time analytics. The proposed system integrates Self-Serve Analytics to empower security analysts and business users with on-demand insights, customizable dashboards, and automated anomaly exploration. To ensure robust protection across evolving threat surfaces, the framework incorporates cybersecurity best practices, including secure model deployment, continuous monitoring, encrypted data flows, and behavioral threat modeling. Additionally, Quantum Machine Learning (QML) components are introduced to evaluate quantum-enhanced classification strategies, demonstrating potential improvements in high-dimensional pattern recognition and anomaly detection. Experimental analysis using multivariate fraud datasets highlights superior performance in recall, precision, and detection latency compared to conventional cloud-based systems. The resulting architecture provides a scalable, secure, and intelligence-driven foundation for next-generation fraud detection in financial ecosystems.
A GRA-Enhanced Cloud AI Framework for Petabyte-Scale Multi-Tenant Environments: Multivariate Classification for Credit Card Fraud Detection and Adaptive Risk Analytics
Petabyte-scale, multi-tenant cloud ecosystems generate massive volumes of heterogeneous data that demand scalable, intelligent, and adaptive risk analytics frameworks. This paper presents a GRA-enhanced Cloud AI framework that integrates multivariate classification models with credit card fraud detection and dynamic risk scoring to support high-throughput, real-time financial security operations. Grey Relational Analysis (GRA) is utilized as a feature relevance mechanism to identify influential behavioral, transactional, and contextual variables across large, multi-tenant datasets, improving both model interpretability and predictive stability. The proposed architecture leverages distributed cloud services and big data engines to support parallelized ingestion, preprocessing, and analytics over petabyte-scale workloads. Multivariate machine learning classifiers—such as gradient-boosted trees, deep neural networks, stacked ensembles, and hybrid GRA-weighted models—enable robust detection of fraudulent patterns while reducing false alarms in high-dimensional environments. Adaptive risk analytics modules continuously update fraud scores using streaming data, ensuring real-time responsiveness to evolving threat behaviors. Experimental evaluations demonstrate substantial improvements in fraud detection accuracy, recall, and processing efficiency compared to conventional rule-based and univariate ML systems. The GRA-driven feature optimization enhances model transparency and reduces computational overhead, making the framework suitable for multi-tenant cloud platforms with varied data distributions and performance constraints. This research contributes a scalable, interpretable, and cloud-ready solution for next-generation financial fraud intelligence and risk monitoring at petabyte scale.
Real-Time GenAI Neural LDDR Optimization on Secure Apache–SAP HANA Cloud for Clinical and Risk Intelligence
This paper presents an integrated, production-ready architecture and evaluation of a GenAI-enabled neural network framework for real-time Low-Latency Data Distribution and Routing (LDDR) optimization deployed on a secure, hybrid Apache–SAP HANA cloud infrastructure tailored for risk analytics and clinical AI workloads. Modern clinical and risk-management applications require millisecond-scale data routing and inference across heterogeneous data sources — electronic health records (EHR), streaming telemetry from medical devices, real-time market feeds, and privacy-sensitive patient registries. LDDR is the class of systems and algorithms that minimize end-to-end latency while maintaining reliability, policy-aware routing, and regulatory compliance. We propose a hybrid approach that combines (1) a lightweight GenAI orchestration layer that performs contextual routing decisions and dynamic policy synthesis, (2) a family of neural-network-driven LDDR models that learn optimal routing and replication policies under variable load and failure patterns, and (3) an underlying secure data plane built on Apache components (Kafka, Flink) integrated with SAP HANA for in-memory transactional/analytical processing and strong data governance. The GenAI agent acts as an adaptive planner that translates high-level clinical or regulatory intents into routing constraints and objectives, while the neural LDDR core maps network/compute observables to actions that minimize latency and maximize utility (e.g., inference freshness, fairness across patient cohorts, or risk-exposure reduction). We detail model choices — lightweight convolutional-recurrent hybrid networks with attention mechanisms for time-series and topological features, reinforcement-learned policy networks for routing decisions, and continual-learning techniques to adapt to distribution shift without violating auditability requirements. The secure infrastructure uses encrypted channels, role-based access control, and SAP HANA’s in-memory tables for fast stateful lookups; Apache components handle stream buffering, backpressure, and exactly-once semantics where needed. We describe an end-to-end training and validation pipeline that uses a combination of synthetic stress traces, anonymized clinical datasets, and replayed production telemetry to produce models that operate within strict latency and privacy constraints. In evaluation across representative clinical-AI tasks (real-time sepsis risk scoring, cardiology monitoring alarms) and financial-risk simulations (intraday liquidity and counterparty-risk monitoring), our system reduced median routing+inference latency by 34–56% compared to baseline static routing and rule-based orchestration, while improving the freshness of inference results (staleness window reduction 22–48%). In scenarios requiring regulatory constraints (GDPR-like data residency, HIPAA-style access controls), the GenAI orchestration achieved policy compliance conversion with >98% accuracy of intent-to-constraint translation and maintained end-to-end audit trails. We analyze failure modes, including distributional drift, mis-specified high-level intents, and catastrophic network partitioning, and present mitigation strategies: uncertainty-aware routing, shadow training, and rollback-safe model updates. Finally, we discuss deployment considerations for the combined Apache–SAP HANA stack: cost-quality trade-offs, observability needs, and recommended SLOs/SLA enforcement techniques. The contribution is a practical blueprint and experimental validation for deploying GenAI-driven LDDR systems in highly regulated, latency-sensitive domains where both performance and governance are paramount.
Low-Latency Data Distribution and Routing (LDDR)
Machine Learning–Enhanced Cloud Cyber Defense for Financial Systems: Intelligent Caching, Automated CI/CD Security, and Risk Classification
Financial systems increasingly rely on cloud-native infrastructures, exposing them to sophisticated cyberattacks that exploit API vulnerabilities, high-volume transaction flows, and distributed network dependencies. Traditional security controls are insufficient for detecting complex threat patterns while simultaneously supporting the stringent performance demands of financial applications. This paper proposes a Machine Learning–Enhanced Cloud Cyber Defense Framework that unifies intelligent caching, automated CI/CD security gates, and multivariate risk classification to enable real-time protection of financial systems. The architecture integrates distributed microservices, behavioral analytics, anomaly detection models, and automated DevSecOps pipelines to reduce risk exposure and accelerate remediation. The proposed framework employs hybrid machine learning models—including gradient boosting, LSTM anomaly detection, and multivariate risk classifiers—to identify malicious traffic, unauthorized access patterns, and fraud indicators across high-speed financial networks. Intelligent caching mechanisms are introduced to minimize latency, optimize model inference time, and ensure resilience against DDoS-like traffic spikes. In parallel, CI/CD pipelines are augmented with automated vulnerability scanning, policy enforcement, and real-time security scoring to ensure that only compliant services progress through deployment stages. Experiments using large-scale financial logs, synthetic attack datasets, and credit risk corpora demonstrate that the framework improves threat detection accuracy by up to 94%, reduces API response latency by 38% through cache optimization, and enhances CI/CD security efficiency by 41%. Additionally, multivariate risk classification provides real-time fraud probability scores, strengthening financial decision-making under high-load conditions. Results confirm that integrating advanced machine learning with cloud-native DevSecOps automation significantly improves the security posture, performance, and operational resilience of modern financial systems.
AI-Integrated Cloud Security and Privacy Framework for Protecting Healthcare Network Information and Cross-Team Collaborative Processes
The rapid evolution of digital healthcare ecosystems and the surge in multi-team collaboration activities have intensified the need for next-generation security and privacy technologies capable of protecting sensitive clinical and operational data. This study introduces an innovative AI-integrated cloud security and privacy architecture engineered to deliver end-to-end protection for healthcare network environments while enabling seamless, trustworthy collaboration among distributed teams. The architecture harnesses cutting-edge machine learning intelligence—combining behavioral threat modeling, context-aware anomaly detection, and predictive cyber-risk analytics—to identify and neutralize security breaches before they escalate. Dynamic and self-adjusting access control mechanisms continuously evaluate user intent and environmental context, while autonomous encryption pipelines ensure uninterrupted, highly secure data movement across multi-layer cloud and network infrastructures. To strengthen data confidentiality, the framework incorporates advanced privacy-preserving AI strategies such as federated learning for decentralized model training without exposure of sensitive records, and differential privacy to deliver rigorous, mathematically backed privacy guarantees. Integrated governance modules provide real-time compliance validation, automated policy orchestration, and end-to-end secured communication channels aligned with global healthcare regulations. Experimental findings demonstrate that the proposed framework significantly enhances resilience against complex cyber threats, minimizes unauthorized access risks, and maintains operational continuity in high-intensity, multi-team healthcare environments. Ultimately, this innovative architecture establishes a future-ready, scalable, and regulation-aligned foundation for intelligent healthcare security and secure collaborative data management in next-generation digital health systems.
Cloud-Native AI Platform for Real-Time Resource Optimization in Governance-Driven Project and Network Operations
Governance-driven digital environments increasingly require intelligent, transparent, and highly efficient systems to manage project and network operations at scale. This study presents a cloud-native AI platform designed to achieve real-time resource optimization while ensuring strict adherence to organizational and regulatory governance standards. The proposed platform integrates machine learning–based forecasting, intelligent workload balancing, and automated orchestration to dynamically allocate resources across distributed project and network ecosystems. Continuous monitoring and analytics-driven decision engines enhance operational visibility, reduce performance bottlenecks, and enable rapid response to system fluctuations. Governance is embedded through policy-based controls, compliance automation, and secure audit mechanisms that maintain accountability and consistency across all operational layers. Experimental evaluation demonstrates that the framework significantly improves resource efficiency, strengthens governance alignment, and enhances overall reliability in complex project and network environments. The platform establishes a scalable and intelligent foundation for next-generation operational management in regulated sectors.
Next-Gen Life Sciences Manufacturing: A Scalable Framework for AI-Augmented MES and RPA-Driven Precision Healthcare Solutions
The convergence of precision medicine and advanced manufacturing necessitates a paradigm shift in life sciences production. Traditional Manufacturing Execution Systems (MES) and manual processes struggle with the complexity, data volume, and personalization requirements of next-generation therapies like Cell and Gene Therapies (CGTs) and stratified biologics. This study proposes and validates a scalable framework integrating Artificial Intelligence (AI) and Robotic Process Automation (RPA) within a next-generation MES to enable precision healthcare manufacturing. Utilizing a mixed-methods approach, we developed a framework comprising an AI-augmented MES core for real-time process control and predictive analytics, an RPA layer for automating high-volume, rule-based tasks, and a digital thread for seamless data integration from patient to product. A quantitative simulation of a CGT manufacturing process demonstrated a 32% reduction in batch failure rates and a 25% decrease in release times. Concurrently, a qualitative case study with industry partners confirmed significant improvements in operational agility, regulatory compliance, and scalability. The findings indicate that the synergistic application of AI and RPA can overcome critical bottlenecks in precision medicine manufacturing, leading to more robust, efficient, and patient-centric production systems. This research provides a validated blueprint for the digital transformation of life sciences operations
A Secure DevSecOps-Driven Cloud Intelligence Framework: Integrating Deep Neural Networks, Distributed Systems, and NLP for AI-First Banking
AI-first banking demands intelligent, scalable, and secure architectures capable of defending against rapidly evolving cyber threats while enabling real-time analytics and automated decision-making. This paper proposes a Secure DevSecOps-Driven Cloud Intelligence Framework that integrates Deep Neural Networks (DNNs), distributed cloud systems, Natural Language Processing (NLP), and continuous security automation to enhance security, resilience, and operational efficiency in modern banking ecosystems. The framework embeds security controls across the development pipeline using DevSecOps principles and leverages Azure DevOps and GitHub automation for continuous integration, delivery, and monitoring. A distributed DNN architecture is utilized for anomaly detection, fraud prediction, and risk scoring, while NLP-driven threat mining extracts actionable insights from logs, user interactions, and financial documents. The system operates on an elastic cloud environment, enabling auto-scaling, microservices orchestration, and real-time data mining across high-volume banking workloads. Experimental results demonstrate improved security posture through automated policy enforcement, faster threat detection via deep learning, and reduced operational overhead due to continuous monitoring and intelligent feedback loops. This unified framework provides a robust foundation for secure, intelligent, and scalable AI-first banking, addressing critical challenges in cybersecurity, data governance, continuous compliance, and distributed system reliability.
AI-Enhanced Ethernet–Lakehouse Convergence: Dynamic Bayesian Models and Real-Time Streaming Intelligence for SAP Workflows
The rapid growth of enterprise data demands architectures that unify low-latency connectivity with scalable analytical intelligence. This work introduces an AI-enhanced framework that converges Ethernet-level data transport with modern lakehouse architectures to optimize SAP-driven business processes. By integrating Dynamic Bayesian Hierarchical Models with real-time streaming pipelines, the system enables continuous probabilistic reasoning, anomaly detection, and adaptive decision-making across heterogeneous operational data sources. The proposed architecture leverages Retrieval-Augmented Generation (RAG), cloud-native orchestration, and intelligent quality assurance to transform SAP workflows into predictive, data-aware ecosystems. Results demonstrate improved data reliability, reduced system latency, and automated insights that enable organizations to operate effectively in both data-rich and data-scarce environments. This research highlights the strategic potential of merging networking intelligence, AI modeling, and lakehouse design to accelerate digital transformation across enterprise landscapes.
Real-Time Cloud Threat Intelligence for AI-First Banking: An Explainable Generative AI Framework for Credit and Risk Modeling using Apache and SAP HANA
In an increasingly digital banking environment, the convergence of real‑time cloud threat intelligence and AI‑driven credit risk modeling offers transformative potential. This work proposes a novel, explainable generative AI framework tailored to an AI‑first banking architecture, leveraging Apache distributed computing and SAP HANA in‑memory database technologies. The framework ingests continuous cyber threat intelligence feeds from the cloud—such as Indicators of Compromise (IoCs), threat-actor behaviors, and adversarial tactics—and integrates them into credit and risk scoring pipelines. A generative model (e.g., a conditional Generative Adversarial Network or transformer‑based LLM) synthesizes threat-informed features, enabling proactive adaptations to credit models in response to emerging cyber risk. Explainability is achieved via post-hoc explanation methods (e.g., SHAP, LIME) and prompt-based natural-language justification modules, ensuring transparency and auditability for regulatory compliance. We evaluate our framework on a simulated banking dataset combined with synthetic threat-stream data. Experiments show that threat-aware generative augmentation improves credit default prediction performance (e.g., AUC) compared to baseline models, while maintaining interpretability. The use of Apache Spark ensures real-time feature engineering and streaming, whereas SAP HANA’s in‑memory capabilities facilitate low-latency inference and decisioning. The contribution of this research lies in bridging cyber threat intelligence with financial risk modeling, providing a real‑time, explainable, scalable system. We discuss advantages (e.g., proactive risk mitigation, improved model robustness) and potential drawbacks (e.g., model complexity, data integration challenges). Finally, we outline future directions including deployment in production banking environments, regulatory validation, adversarial robustness, and continuous feedback loops.
Federated Deep Learning with Privacy Guarantees for Large-Scale Distributed AI Applications
The rapid proliferation of data-generating devices and large-scale intelligent systems has led to an exponential increase in distributed data across mobile, embedded, and IoT environments. Traditional centralized machine learning approaches rely heavily on aggregating raw data to central servers, raising significant privacy, security, and communication-efficiency concerns. Federated Deep Learning (FDL) has emerged as a transformative paradigm enabling collaborative model training without the need to share sensitive local datasets. Despite its promise, several unresolved challenges persist, including data heterogeneity, communication overhead, adversarial threats, and insufficient formal privacy guarantees. This research paper presents a comprehensive framework for Federated Deep Learning with Privacy Guarantees (FDL-PG) designed explicitly for large-scale distributed AI applications such as smart healthcare systems, edge-based autonomous vehicles, intelligent IoT ecosystems, and industrial automation networks. The proposed framework integrates advanced deep learning architectures with rigorous privacy-preserving techniques, including secure aggregation, differential privacy, homomorphic encryption, and adversarial robustness strategies. By incorporating secure aggregation protocols, the system ensures that individual model updates remain concealed even from the central coordinating server. Differential privacy mechanisms further introduce calibrated noise to gradients and model parameters, thereby preventing sensitive information leakage while maintaining task accuracy. Homomorphic encryption plays a complementary role by enabling computation on encrypted updates, ensuring data confidentiality even during intermediate processing steps.
GenAI-Enabled Neural Network–Driven Real-Time LDDR Optimization on Secure Apache–SAP HANA Cloud Infrastructure for Risk and Clinical AI
A Unified AI–Cloud Architecture for Healthcare, Finance, and Agriculture Leveraging ML, NLP, and Disease Analytics
The rapid proliferation of digital ecosystems across healthcare, finance, and agriculture demands unified, intelligent, and scalable architectures capable of delivering sustainable, data-driven decision support. This study proposes a Unified AI–Cloud Framework that integrates Machine Learning (ML), Natural Language Processing (NLP), Open Banking APIs, and agricultural disease analytics within a secure, multi-cloud environment. The framework leverages cloud-native microservices, real-time data pipelines, federated learning models, and secure API gateways to enable cross-sector interoperability. In healthcare, ML-driven clinical prediction and NLP-based patient record mining enhance diagnostic accuracy and operational efficiency. In finance, Open Banking integration supports intelligent credit scoring, transaction anomaly detection, and personalized risk-aware services. In agriculture, convolutional neural networks (CNNs) and spectral analytics are used for early detection of crop diseases such as cotton leaf disease. The proposed architecture emphasizes sustainability through energy-efficient model deployment, privacy-preserving data governance, and adaptive resource provisioning. Experimental results and simulations demonstrate improved decision accuracy, reduced latency, and enhanced system resilience across all three domains. The framework establishes a scalable foundation for future AI-driven, cross-industry digital transformation.
Trustworthy Multimodal AI: Unifying Vision–Language Models with Verifiable Safety Constraints
Multimodal Artificial Intelligence (AI), particularly Vision–Language Models (VLMs), has become a powerful foundation for tasks requiring joint reasoning over images, text, and structured semantics. While these systems demonstrate impressive capabilities across classification, captioning, visual question answering, and embodied perception, their increasing integration into safety-critical domains—such as autonomous systems, healthcare diagnostics, assistive robotics, and intelligent surveillance—raises profound concerns about reliability, explainability, robustness, and societal impact. Traditional VLMs heavily rely on deep neural architectures that excel in representation learning but lack deterministic, verifiable constraints capable of governing safe behavior. As a result, these models may propagate biases, hallucinate content, misinterpret visual cues, or generate unsafe outputs without reliable mechanisms for detecting, preventing, or correcting harmful actions. This research addresses these challenges by proposing a unified framework for trustworthy multimodal AI, integrating verifiable safety constraints at both the representation and decision-making layers of VLMs. The proposed framework introduces a three-level architecture that embeds formal safety constraints, logic-driven guardrails, and robust uncertainty quantification into contemporary vision–language pipelines. First, a Multimodal Consistency Engine ensures structural alignment between visual and textual representations through contrastive reasoning, cross-modal attention validation, and semantic discrepancy detection. Second, a Safety Verification Layer incorporates symbolic rules, temporal logic constraints, and counterfactual analysis to assess whether predicted outputs comply with predefined ethical, domain-specific, and operational safety standards. Third, a Risk-Adaptive Decision Module performs introspective uncertainty estimation, calibration, anomaly detection, and fallback-response generation to prevent unsafe or ambiguous outputs. Together, these layers create an end-to-end, expandable framework that allows VLMs to operate within codified boundaries of trustworthy behavior.
Hybrid Neuro-Symbolic Pipelines for Explainable Decision-Making in Mission-Critical Systems
Mission-critical systems—such as autonomous vehicles, aerospace navigation, medical diagnostics, disaster-response robots, and critical infrastructure monitoring—demand AI models that are not only highly accurate but also robust, transparent, and explainable under extreme operational conditions. Traditional deep learning architectures often lack interpretability, while purely symbolic approaches struggle with scalability and uncertain environments. This research proposes a Hybrid Neuro-Symbolic Pipeline (HNSP) that unifies the perceptual strength of neural networks with the logical consistency of symbolic reasoning to achieve explainable, auditable, and verifiable decision-making in real-world mission-critical domains. The proposed HNSP architecture integrates four major components: (1) Perception Layer, powered by deep neural networks for extracting high-dimensional features from multimodal inputs such as images, telemetry signals, sensor fusion outputs, or text reports; (2) Concept Extraction Layer, which converts continuous neural representations into discrete symbolic concepts using disentanglement techniques, attention-based concept activation vectors, or clustering-based symbolic abstraction; (3) Symbolic Reasoning Engine, which leverages rule-based logic, knowledge graphs, ontologies, and constraint satisfaction models to perform transparent reasoning aligned with domain-specific safety protocols; and (4) Explainability & Audit Module, which generates human-interpretable decision logs, causal reasoning traces, counterfactual justifications, and rule activation visualizations to ensure traceability and regulatory compliance. The proposed pipeline addresses key challenges in mission-critical AI: reliability under distribution shifts, robustness against adversarial perturbations, traceability of inference, and incorporation of domain rules that cannot be learned solely from data. By enabling neural and symbolic components to interact bidirectionally, the system ensures that high-level reasoning can correct low-level perception errors and provide interpretable feedback. Furthermore, the architecture supports adaptive learning, where symbolic feedback constrains neural model updates, improving generalization and safety.
Generative Agents for Human-Aligned Decision Support in Complex Domains
The rapid advancement of generative AI—particularly large language models (LLMs), multimodal transformers, and autonomous agent frameworks—has opened new avenues for decision support in complex, high-stakes domains such as healthcare, finance, cybersecurity, disaster response, and large-scale industrial management. However, a major challenge remains: ensuring that generative agents not only produce high-quality analytical outputs but also remain truly human-aligned, interpretable, context-aware, and ethically constrained. This research paper presents a comprehensive framework for Human-Aligned Generative Decision Support Agents (HAG-DSA) that leverage advanced generative modeling, cognitive planning, interactive reasoning, and domain knowledge integration to assist human decision-makers in environments characterized by uncertainty, dynamic changes, and multidimensional constraints. The proposed framework conceptualizes generative agents as hybrid intelligent systems capable of synthesizing structured and unstructured data, forecasting multiple scenarios, generating counterfactual explanations, and producing recommendations aligned with human values and domain-specific policies. Key design principles include value alignment, explainability, robustness, uncertainty quantification, and human-in-the-loop co-assessment. Unlike conventional rule-based or predictive models, generative agents can simulate diverse possibilities, summarize complex evidence, identify latent risks, and enhance human situational awareness by creating interpretable decision narratives.
Secure Federated Foundation Models with Differentially Private Parameter Exchange
The rapid deployment of large-scale foundation models across industry, healthcare, finance, and critical infrastructure has accelerated the need for privacy-preserving, distributed training paradigms. Traditional centralized training pipelines require aggregating sensitive data into a single location, exposing individuals and organizations to significant privacy, security, and compliance risks. Federated Learning (FL) has emerged as a robust alternative, enabling collaborative model training without sharing raw data. However, when extended to foundation models—large-parameter architectures such as LLMs, multimodal transformers, and domain-specific large encoders—FL faces substantial challenges in communication scalability, model leakage, adversarial inference, and system-level security. This research addresses these critical issues by introducing a secure federated foundation model framework that incorporates differentially private parameter exchange, ensuring mathematically provable privacy guarantees while preserving model utility. The proposed framework integrates advanced Differential Privacy (DP) mechanisms, such as Gaussian noise calibration, privacy budget accounting, and adaptive clipping, directly into the parameter-exchange pipeline executed at each participating client. Instead of naïvely sharing gradients or parameter deltas, the system enforces per-round privacy bounds using dynamic ε-δ budget allocation, optimizing noise injection according to model sensitivity and gradient sparsity. To address the unique computational and communication burdens associated with foundation models, we propose a hybrid compression–encryption protocol involving secure aggregation, quantization-aware DP clipping, and low-rank parameter exchange. This design reduces bandwidth consumption while enhancing end-to-end security against inference attacks, model reconstruction, and poisoning. The proposed system advances the current state of privacy-preserving federated foundation models by offering a unified architecture with verifiable privacy, scalable deployment, and resilience to adversarial behavior. It bridges a critical gap between theoretical DP formulations and their practical implementation in high-capacity models. Beyond technical contributions, the framework supports emerging regulatory and ethical requirements related to AI safety, digital privacy, and responsible data-sharing ecosystems. By enabling secure, collaborative training of foundation models across globally distributed data sources, this research paves the way for future-ready AI systems that uphold privacy by design while delivering state-of-the-art performance.
Zero-Shot and Few-Shot Learning Algorithms for Autonomous Robotics in Unstructured Environments
Autonomous robots operating in unstructured environments must navigate unpredictable conditions, dynamic obstacles, and novel tasks that cannot be fully anticipated during training. Traditional deep learning models rely heavily on large, labeled datasets and struggle when faced with unseen scenarios, limiting their adaptability and real-world deployment. This paper proposes a comprehensive framework for Zero-Shot and Few-Shot Learning Algorithms tailored for autonomous robotics, enabling robots to generalize from minimal or no prior examples. The framework integrates semantic embedding models, task-conditioned policy networks, meta-learning strategies, and multimodal perception modules to support rapid adaptation in unstructured and continuously evolving environments. Zero-shot learning is achieved through semantic-to-action mapping using knowledge graphs, language models, and attribute-based embeddings, while few-shot learning relies on gradient-based meta-learning, metric learning, and prototype adaptation for new robotic tasks. A unified training pipeline leverages multimodal sensory inputs—vision, LiDAR, proprioception, and natural language instructions—to build robust representations for manipulation, locomotion, and navigation. Experimental evaluations on simulated and real-world robotic platforms demonstrate that the proposed algorithms significantly outperform conventional deep RL and supervised models in task generalization, sample efficiency, and resilience to environmental variability. The results highlight the potential of zero-shot and few-shot learning to accelerate the development of scalable, adaptable, and intelligent autonomous robotic systems capable of reasoning, learning, and performing reliably in complex, unstructured scenarios.
AI-Augmented Cloud Performance Metrics with Integrated Caching and Transaction Analytics for Superior Project Monitoring and Quality Assurance
The increasing complexity of modern digital ecosystems demands intelligent, scalable, and real-time monitoring solutions capable of ensuring service reliability, operational transparency, and high-quality project outcomes. This paper presents an AI-augmented cloud performance metrics framework that seamlessly integrates adaptive caching and transaction analytics to enhance project monitoring and quality assurance. The proposed architecture leverages machine learning–driven anomaly detection, predictive resource optimization, and dynamic caching strategies to reduce latency and improve data consistency across distributed cloud environments. Transaction analytics are incorporated to evaluate integrity, throughput, and risk factors, enabling early detection of process deviations and performance bottlenecks. Real-time dashboards and automated quality assessment indicators further empower decision-makers with actionable insights for continuous improvement. Experimental evaluations demonstrate that the integrated AI–cloud framework significantly enhances responsiveness, accuracy, and reliability compared to traditional cloud monitoring systems, making it a robust approach for high-demand project management environments.
Comprehensive AI Governance Framework: A Strategic Approach for Organizations in Dynamic Regulatory Environments
Artificial intelligence systems are transforming business operations and societal functions, creating urgent demands for robust governance frameworks. Organizations worldwide face mounting pressure to ensure responsible AI development and deployment while navigating increasingly complex regulatory landscapes. This white paper presents a comprehensive, jurisdiction-agnostic AI governance framework addressing six critical risk domains: privacy, bias, transparency, security, compliance, and societal trust. We propose eight actionable recommendations supported by a phased implementation roadmap, enabling organizations to build adaptive governance systems that balance innovation with responsibility. This framework integrates technical controls, organizational processes, and stakeholder engagement to help organizations transform AI governance from a compliance burden into a strategic advantage.
Leveraging Business Intelligence and AI-Driven Analytics to Strengthen U.S. Cybersecurity Infrastructure
The United States government and the private sector's increasing reliance on digital technologies has heightened their vulnerability to advanced cyber threats. Traditional reactive cybersecurity measures are insufficient for mitigating the threat posed by ever-changing attack vectors, and there is a strong need for predictive, data-driven defense measures. This paper discusses the opportunities for using Business Intelligence (BI) systems and Artificial Intelligence (AI)-driven analytics to enhance the national cybersecurity infrastructure by proactively detecting and shaping threats, enabling intelligent cybersecurity monitoring, and enabling predictive assessment. The research combines data from the available literature and national datasets to develop a BI-AI analytical framework that can detect anomalies, predict potential breaches, and reduce response times across distributed networks. Emphasis is placed on integrating machine learning (ML) models, such as random forests, support vector machines (SVMs), and neural networks, into BI dashboards to enhance situational awareness and automated decision-making. A comparative evaluation shows that AI-enhanced BI systems outperform conventional rule-based methods in terms of accuracy, precision, and scalability [3]; [7]. The research provides an important signal that unifying threat intelligence platforms, cloud-based BI infrastructure, and explainable AI is essential to ensuring the resilience and transparency of cybersecurity operations. Moreover, the study highlights the importance of public-private partnerships and policy-based governance in implementing data-centric defense frameworks at the national level. Findings offer a blueprint for introducing predictive analytics into U.S. cybersecurity ecosystems to reduce systemic vulnerabilities and enable adaptive, real-time defense strategies.
Rokeya Begum Ankhi, Mahamuda khanom, Mohammad Majharul Islam Jabed, Ahmed Sohaib Khawer, Sharmin Ferdous, Amit Banwari Gupta
Leveraging Oracle AI and SAP Integration for a Secure Cloud Framework in Electronic Health Record Management
The rapid digital transformation of healthcare necessitates secure, intelligent, and interoperable systems for managing Electronic Health Records (EHRs). This study proposes a robust cloud-based framework that integrates Oracle Artificial Intelligence (AI) capabilities with SAP enterprise systems to enhance security, scalability, and data-driven decision-making in EHR management. The framework leverages Oracle Cloud Infrastructure (OCI), Oracle Machine Learning (OML), and SAP Health modules to ensure seamless data exchange, automated analytics, and real-time clinical insights. Advanced AI models enable anomaly detection, patient data classification, and predictive risk analysis, while SAP integration ensures operational continuity, workflow automation, and compliance with healthcare standards. Security is strengthened through encryption, identity access management, and intelligent monitoring embedded within OCI’s security architecture. The proposed system demonstrates improved data integrity, enhanced interoperability, and optimized healthcare operations, offering a reliable solution for next-generation EHR management in modern healthcare institutions.