International Journal of Multidisciplinary Research in Science, Engineering, Technology & Management
The International Journal of Multidisciplinary Research in Science, Engineering, Technology & Management (IJMRSETM) is a bimonthly, peer-reviewed, open-access academic journal dedicated to the advancement and dissemination of knowledge in science, engineering, technology, and management. The journal welcomes high-quality original research, review papers, case studies, and short communications. Scope of the Journal IJMRSETM covers a wide range of topics including but not limited to: Science: Physics, Chemistry, Biology, Environmental Science, Life Sciences Engineering: Mechanical, Civil, Electrical, Electronics, Computer Science, etc. Technology: AI, IoT, Data Science, Cybersecurity, Software Engineering, etc. Management: Business Administration, Operations, Finance, HR, Marketing, Strategic Management
Governance-Aware Secure Architecture for Real-Time Enterprise Data Exchange across Financial Healthcare and Advertising Platforms Using Generative AI
The rapid digital transformation of enterprises has intensified the need for secure, interoperable, and governance-aware data exchange across heterogeneous domains such as financial services, healthcare systems, and digital advertising platforms. These sectors manage highly sensitive information, including personal health records, financial transactions, and behavioral analytics, making them prime targets for cyber threats, data misuse, and regulatory non-compliance. Traditional data exchange frameworks often lack real-time governance enforcement, cross-domain trust mechanisms, and intelligent automation required to support modern enterprise ecosystems. This paper proposes a governance-aware secure architecture that integrates Generative AI with cloud-native microservices, zero-trust security models, and policy-driven data exchange protocols to enable real-time and compliant enterprise communication across financial, healthcare, and advertising environments. The proposed architecture introduces a multi-layered framework comprising secure data ingestion pipelines, AI-driven governance engines, federated identity management, privacy-preserving computation, and intelligent monitoring systems. Generative AI models are utilized for adaptive policy enforcement, anomaly detection, automated compliance reporting, and contextual data transformation. The architecture supports interoperability across enterprise resource planning (ERP), healthcare information systems, and programmatic advertising platforms while ensuring adherence to regulatory standards such as GDPR, HIPAA-like policies, and financial compliance requirements. Results indicate that integrating Generative AI within governance-aware architectures significantly enhances threat detection accuracy, reduces policy violations, and improves cross-domain data exchange efficiency. The framework also demonstrates improved scalability, reduced latency in secure data transactions, and enhanced transparency through audit-ready logs and explainable AI governance mechanisms. By combining real-time analytics, zero-trust principles, and AI-driven compliance automation, the proposed architecture provides a unified and resilient solution for secure enterprise data ecosystems. This research contributes a novel cross-domain architecture that bridges governance, security, and AI-enabled automation for modern enterprise environments. The findings highlight the potential of Generative AI to transform enterprise data governance by enabling adaptive, intelligent, and secure real-time data exchange across multiple regulated sectors.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
AI-Powered Cloud-Native ERP Enterprise Systems with Information Retrieval Decision Analytics Cybersecurity and Zero-ETL Analytics
Modern enterprises increasingly rely on intelligent and resilient digital infrastructures to support real-time decision-making across finance, healthcare, manufacturing, and retail domains. Traditional enterprise resource planning (ERP) systems, while central to operational management, often suffer from rigid architectures, delayed analytics, and fragmented data pipelines. This paper proposes an integrated AI-powered cloud-native enterprise framework that enhances ERP operations through advanced information retrieval, decision analytics, cybersecurity, and Zero-ETL real-time analytics. The framework leverages microservices, container orchestration, and serverless computing to enable scalable and adaptive enterprise platforms. AI-driven modules support predictive analytics, anomaly detection, and reinforcement learning–based decision intelligence for financial risk management, fraud detection, and operational optimization. Information retrieval models using natural language processing and semantic search improve enterprise knowledge discovery and contextual decision support. A Zero-ETL architecture enables direct querying of operational data from cloud warehouses, reducing latency and enabling real-time dashboards. Cybersecurity is strengthened through zero-trust architecture, AI-based threat detection, and compliance-aware governance mechanisms. The proposed system demonstrates improved responsiveness, scalability, and decision accuracy compared to conventional enterprise platforms. This study contributes a unified enterprise architecture that integrates AI, cloud-native design, and real-time analytics to support secure, intelligent, and resilient enterprise operations.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
Post Covid Teaching Approaches and Methodologies
Education is the pillar of every country’s development. The COVID-19 has produced a transformation in society that has, in turn, influenced the field of education. There has been a paradigm shift from face to face teaching to technology based curriculum delivery. Online learning during covid-19 pandemic has awakened and affirmed the necessity of learning based on digital technology. The face to face learning is still necessary. As a result, the learning pattern developed during post-covid pandemic combines face to face and online learning called as blended learning. It is a way of learning, combining the traditional way of face-to-face classroom instruction and teaching with the help of digital learning platforms and tools. This mode of learning is also known as Hybrid Learning, as it integrates technology and digital media with the traditional teacher-led classroom activities, providing more flexibility for students to customize their learning experience. The online mode of classes can be categorically classified under two heads, synchronous and asynchronous, based on the conduction of the classes. Synchronous mode refers to the type of learning where students and teachers are present at the exact location and at the same time for teaching and learning. This comprises in-person classes (where teachers and students are present in the same classroom), online meetings and live streaming of classes or demonstrations on Zoom, MS Teams, Google meet, and other platforms. Precisely it is a “real-time” type of learning where a group of learners is engaged simultaneously. Hence, it enables collaborations amongst the students and teachers to ask doubts and get them resolved on the spot. For example, webinars, online classrooms, and video conferences are examples of synchronous classes. Asynchronous mode refers to the universal form of teaching and learning that does not happen simultaneously or in the same classroom. The students are not present in the class at a prescribed time. However, they have access to the previously recorded lecture videos of their teachers in addition to online study materials. Students can respond through emails and any social media network. The teachers generally record their classes. This recording is made available to the students; it is a learner-centered approach, where the students can undertake any course without fulfilling the criteria of being present at the same time and exact location as the tutor. For instance, blogs, you tube videos, and online lectures are examples of asynchronous classes. One cannot deny the importance of the offline education system; the future would be directed towards blended learning guided towards online pivots and a digital mindset.
AI-Driven Cloud Enterprise Decision Platform for Predictive Fraud Detection and Secure Biometric Authentication across Networks
The rapid digitalization of enterprise ecosystems has significantly increased exposure to cyber fraud, identity theft, and unauthorized network access. Traditional rule-based security mechanisms are insufficient to detect sophisticated fraud patterns across distributed cloud and mobile environments. This paper proposes an AI-driven cloud enterprise decision platform that integrates predictive fraud analytics, secure biometric authentication, and cross-network intelligence to enhance organizational security and decision-making. The proposed architecture leverages machine learning, deep learning, and behavioral analytics within a scalable cloud infrastructure to identify anomalies, prevent financial fraud, and ensure secure identity verification across enterprise systems. The framework combines biometric modalities such as facial recognition, fingerprint scanning, and behavioral biometrics with real-time fraud detection engines. Data from enterprise resource planning (ERP) systems, mobile devices, financial applications, and network logs are aggregated into a centralized cloud data platform. AI models perform predictive risk scoring, anomaly detection, and contextual decision analysis. A zero-trust network model and encryption protocols ensure secure data transmission and regulatory compliance. The system incorporates explainable AI (XAI) and compliance-driven governance to support transparency, auditability, and ethical decision automation. Experimental simulations demonstrate improved fraud detection accuracy, reduced false positives, and enhanced authentication reliability compared with conventional enterprise security systems. The results highlight the platform’s effectiveness in enabling proactive threat detection, secure identity verification, and real-time decision intelligence. The proposed approach provides a scalable and compliance-aware solution for enterprises operating across cloud, mobile, and networked environments.
AI-Enabled Enterprise Platforms Integrating Financial Risk Analytics, Mobile Healthcare Communication, Intelligent Manufacturing, and Cybersecure SAP Systems
Artificial Intelligence (AI) has emerged as a transformative force in enterprise platforms, enabling organizations to integrate complex operational domains such as financial risk analytics, mobile healthcare communication, intelligent manufacturing, and cybersecure SAP systems. Modern enterprises operate within data-intensive, interconnected ecosystems where real-time decision-making, regulatory compliance, and system resilience are critical. AI-enabled enterprise platforms leverage machine learning, predictive analytics, natural language processing, and intelligent automation to enhance efficiency, accuracy, and adaptability across organizational functions. In financial risk analytics, AI improves forecasting, fraud detection, and credit assessment. In mobile healthcare communication, AI enhances patient engagement, diagnostics, and remote monitoring. Intelligent manufacturing benefits from AI-driven predictive maintenance, quality optimization, and supply chain coordination, while cybersecure SAP systems rely on AI to detect anomalies, prevent cyber threats, and ensure business continuity. This paper explores the integration of these domains within unified enterprise platforms, examining their technological foundations, strategic benefits, and implementation challenges. A comprehensive literature review and research methodology are presented to analyze current practices and future trends. The study concludes that AI-enabled enterprise platforms offer significant competitive advantages but require careful governance, ethical oversight, and robust cybersecurity strategies to ensure sustainable adoption.
A Secure AI/ML-Enabled Cloud Enterprise Framework with SAP and Data Warehousing for Mobile Healthcare Communication and Financial Web Platforms
The rapid convergence of mobile healthcare communication systems and financial web platforms has intensified the demand for secure, intelligent, and scalable cloud enterprise architectures. This paper proposes a secure AI and machine learning–enabled cloud enterprise framework that integrates SAP-based enterprise systems and centralized data warehousing to support real-time analytics, intelligent automation, and compliance-aware operations across healthcare and financial domains. The framework leverages cloud-native microservices, AI-driven decision intelligence, and secure mobile communication channels to enable reliable data exchange and adaptive service delivery. Advanced security mechanisms, including identity and access management, encryption, and policy-driven governance, are embedded to address data privacy, regulatory compliance, and trust requirements. The incorporation of SAP data warehousing enhances enterprise-wide data integration, reporting, and analytical consistency, while machine learning models support predictive healthcare insights and financial risk detection. The proposed framework emphasizes fairness, transparency, and auditability to ensure equitable and responsible AI adoption. Experimental analysis and architectural evaluation demonstrate improved scalability, data integrity, and operational efficiency, making the framework suitable for modern cloud-enabled healthcare and financial enterprise environments.
Secure Generative AI–Enabled Cloud Lakehouse for SAP Financial and Healthcare Analytics with Tableau-Driven Decisions
The increasing volume, velocity, and sensitivity of enterprise data in financial and healthcare domains demand advanced analytics architectures that are scalable, secure, and intelligent. Traditional SAP-centric data warehouses struggle to support real-time insights, unstructured data processing, and advanced artificial intelligence (AI) workloads. This paper proposes a Secure Generative AI–Enabled Cloud Lakehouse architecture integrated with SAP financial and healthcare data, enhanced by Tableau-driven decision intelligence. The architecture combines the flexibility of cloud data lakes with the governance and performance of data warehouses, while embedding generative AI for predictive, prescriptive, and narrative analytics. Security and compliance are enforced through encryption, role-based access control, data masking, and regulatory alignment with standards such as HIPAA and SOX. Tableau serves as the visualization and decision layer, enabling self-service analytics and AI-augmented insights for business and clinical stakeholders. Experimental evaluation and use-case analysis demonstrate improved query performance, enhanced data accessibility, reduced time-to-insight, and stronger governance compared to traditional architectures. The results confirm that the proposed approach effectively bridges SAP transactional systems, advanced analytics, and secure AI-driven decision-making in highly regulated industries.
AI-Driven Cloud and LLM Architectures for Risk-Sensitive Banking Analytics and Secure Web Applications in 5G Environments
The rapid digitization of banking services and trade platforms has created unprecedented opportunities for efficiency and accessibility, while simultaneously introducing complex financial, operational, and cybersecurity risks. Traditional fraud detection systems and risk management methods are increasingly inadequate in processing high-volume, high-velocity, and multi-modal data streams, particularly in real-time 5G-enabled environments. This study proposes an AI-driven cloud and Large Language Model (LLM) architecture for risk-sensitive banking analytics and secure web applications. The framework integrates predictive AI for anomaly detection, generative AI for simulating complex risk scenarios, and LLMs for analyzing unstructured data, generating interpretable insights, and supporting compliance efforts. Secure Extract–Transform–Load (ETL) pipelines standardize and anonymize data prior to analysis, while cloud-native deployment ensures scalability, fault tolerance, and low-latency performance. Privacy-preserving mechanisms, including differential privacy and secure multi-party computation, protect sensitive financial and transactional data. Experimental evaluations using real and simulated banking datasets indicate detection accuracy exceeding 95%, significant reductions in false positives, enhanced operational efficiency, and improved interpretability for human analysts. This work provides a comprehensive blueprint for deploying adaptive, secure, and intelligent financial analytics systems capable of operating efficiently in 5G networks, combining risk-sensitive decision-making with privacy-preserving mechanisms for modern banking and trade operations.
Secure GAN-Driven Cloud Intelligence for Big Data Healthcare Analytics with Randomized Interference Evaluation
The growing adoption of cloud-based healthcare platforms and large-scale medical data analytics has introduced new challenges related to security, robustness, and reliability of artificial intelligence models. This paper presents a secure GAN-driven cloud intelligence framework designed for big data healthcare analytics with a focus on randomized interference evaluation. The proposed approach leverages Generative Adversarial Networks (GANs) to model complex data distributions, enhance anomaly detection, and generate synthetic healthcare data for robust testing and validation. Cloud-native security mechanisms, including encryption, access control, and isolation policies, are integrated to protect sensitive patient information and ensure regulatory compliance. Randomized interference evaluation is employed to assess model resilience under data perturbations, adversarial noise, and workload variability, enabling systematic analysis of stability and performance degradation. Experimental results demonstrate improved robustness, scalability, and fault tolerance of healthcare analytics pipelines under diverse interference scenarios. The framework provides a reliable foundation for secure, scalable, and trustworthy AI-driven healthcare analytics in cloud environments.
Secure AI-Driven Cloud Framework for SAP-Based Healthcare Business Processes and Big Data Analytics
The rapid adoption of cloud technologies in healthcare enterprises has accelerated the migration of SAP-based business processes and large-scale data analytics to distributed cloud environments. However, this transformation introduces critical challenges related to data security, regulatory compliance, system scalability, and intelligent threat detection. This paper proposes a Secure AI-Driven Cloud Framework designed to support SAP-based healthcare business processes while enabling robust big data analytics. The framework integrates AI-powered security mechanisms, including anomaly detection and intelligent access control, with governed cloud data platforms to ensure data integrity, confidentiality, and availability. Secure APIs and network-aware cloud orchestration enable seamless interoperability across SAP systems, healthcare applications, and analytics pipelines. The proposed architecture supports real-time data processing, policy-driven governance, and compliance with healthcare regulations such as HIPAA-aligned security principles. Experimental evaluation and architectural analysis demonstrate improved threat detection accuracy, scalable data processing, and reduced operational risk. The framework provides a resilient foundation for secure digital transformation in healthcare enterprises leveraging SAP and cloud-native analytics platforms.
End-to-End Intelligent Automation Using AI and Machine Learning in DevOps Pipelines for Real-Time Analytics
<span style="font-size: 10.0pt; line-height: 115%; font-family: 'Calibri','sans-serif'; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; color: black; mso-themecolor: text1; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-weight: normal;">The growing complexity of modern software systems and the demand for rapid, data-driven decision-making have accelerated the adoption of intelligent automation within DevOps pipelines. This paper presents an end-to-end intelligent automation framework that integrates artificial intelligence (AI) and machine learning (ML) to enable real-time analytics and autonomous decision support across the software delivery lifecycle. The proposed approach embeds ML-driven monitoring, anomaly detection, and predictive analytics into continuous integration and continuous deployment (CI/CD) pipelines, allowing systems to adapt dynamically to changing workloads and operational conditions. By leveraging automated feedback loops, the framework improves deployment efficiency, system reliability, and operational visibility while reducing manual intervention. Experimental observations demonstrate enhanced pipeline performance, faster incident detection, and improved decision-making accuracy. The results highlight the effectiveness of AI- and ML-enabled DevOps automation in supporting scalable, resilient, and data-driven enterprise systems.
Secure Cloud-Native AI Finance Architectures with SAP Integration for Real-Time Machine Learning Feature Engineering and Identity-Aware Access Control in Healthcare
The convergence of cloud computing, artificial intelligence, and enterprise resource planning has transformed financial operations within healthcare organizations, enabling real-time analytics, intelligent automation, and data-driven decision-making. However, the integration of AI-powered finance platforms with SAP systems in cloud-native environments introduces significant challenges related to security, identity governance, data privacy, and regulatory compliance. This paper presents secure cloud-native AI finance architectures with SAP integration for real-time machine learning feature engineering and identity-aware access control in healthcare. The proposed architecture integrates SAP financial and operational data with cloud-native AI pipelines to support real-time feature engineering, model training, and inference for use cases such as revenue cycle optimization, fraud detection, and cost forecasting. Machine learning–driven feature stores and streaming analytics enable low-latency insights while maintaining data consistency and auditability. Security is enforced through identity-aware access control, zero-trust principles, and fine-grained authorization across SAP, cloud platforms, and AI services, ensuring that sensitive healthcare and financial data is accessed only by authorized users and systems. By combining SAP integration, real-time machine learning, and robust identity and access management, the architecture delivers scalable, compliant, and resilient AI finance platforms tailored for healthcare environments. The proposed approach demonstrates how cloud-native security and AI capabilities can enhance financial transparency, operational efficiency, and cyber resilience in mission-critical healthcare systems.
Leveraging Large Language Models in a Secure AWS Cloud Framework for Federated Learning–Driven Predictive Analytics across Financial and Healthcare Domains
The increasing adoption of data-driven intelligence in financial and healthcare systems raises critical challenges related to data privacy, security, and regulatory compliance. Centralized machine learning approaches often fail to address these concerns due to the sensitive nature of financial records and electronic health data. This paper proposes a secure AWS cloud-based framework that leverages Large Language Models (LLMs) and federated learning to enable predictive analytics across distributed financial and healthcare domains. The proposed architecture integrates privacy-preserving federated learning mechanisms with LLM-driven analytics to support real-time insights without exposing raw data to centralized repositories. AWS-native security services, including identity and access management, encryption, secure data storage, and monitoring, are employed to ensure compliance with industry regulations such as HIPAA and PCI-DSS. The framework supports scalable model training, secure inference, and adaptive risk-aware analytics across heterogeneous data sources. Experimental analysis demonstrates that the proposed approach enhances predictive accuracy while significantly reducing data leakage risks and communication overhead. This research highlights the potential of combining LLMs and federated learning within a secure cloud environment to deliver trustworthy, real-time predictive analytics for mission-critical financial and healthcare applications.
AI-Augmented Network Security and Fraud Detection Framework for Cloud-Based Financial Markets
Cloud-based financial markets have transformed global trading, banking, and investment operations by enabling high-frequency transactions, real-time analytics, and scalable infrastructure. However, this transformation has also introduced complex cybersecurity threats and sophisticated fraud mechanisms that traditional rule-based security systems struggle to detect. This paper proposes an AI-augmented network security and fraud detection framework designed specifically for cloud-based financial market infrastructures. The framework integrates machine learning and deep learning techniques with real-time network traffic analysis, behavioral analytics, and transaction monitoring to provide proactive threat detection and fraud prevention. By combining supervised learning for known attack patterns with unsupervised anomaly detection for zero-day threats, the proposed system enhances detection accuracy while reducing false positives. The framework is designed for cloud-native deployment, ensuring scalability, low-latency processing, and compliance with financial regulations. Experimental evaluation using simulated financial market data and network traffic demonstrates significant improvements in fraud detection precision, cyber threat visibility, and response time compared to traditional security models. The study highlights the importance of AI-driven security analytics in safeguarding cloud-based financial markets and provides practical insights into implementing intelligent, adaptive, and resilient security architectures.
Cloud-Enabled AI Marketing Analytics: Machine-Learning Marketing Mix Modeling with Embedded Cybersecurity and SAP HANA Integration
This study presents a cloud-enabled AI marketing analytics framework that integrates machine-learning–driven Marketing Mix Modeling (MMM) with advanced cybersecurity controls and SAP HANA–based data processing. The framework leverages scalable cloud infrastructure to unify heterogeneous marketing, operational, and customer datasets while enabling automated feature engineering, causal inference, and predictive modeling for optimized budget allocation. SAP HANA’s in-memory architecture accelerates real-time analytics, supporting high-volume data ingestion and rapid model iteration. To address data security risks inherent in cloud-based analytics, the system incorporates embedded cybersecurity measures, including identity and access management, encryption, secure API gateways, and continuous threat monitoring. Experimental results demonstrate improved model accuracy, faster computation, and enhanced data protection compared to traditional on-premise MMM approaches. The proposed architecture offers a secure, high-performance solution for organizations seeking data-driven marketing optimization in dynamic, digitally connected environments.
Deep Transfer Learning for Secure Healthcare Modernization Quality-Assured Integrated Maintenance and Cyber-Threat Intelligence for Data-Scarce Regions
Healthcare systems in data-scarce regions face dual challenges: limited annotated clinical data and rising cyber threats that compromise safety and continuity of care. This paper proposes a Deep Transfer Learning (DTL) framework that integrates quality-assured predictive maintenance for healthcare infrastructure with an embedded cyber-threat intelligence (CTI) module to secure medical devices and data flows. Our approach leverages domain-adaptive transfer learning, synthetic data augmentation, and federated learning to overcome limited local datasets while preserving patient privacy. The maintenance component uses pre-trained deep predictive models fine-tuned to local sensor signatures and operational logs, enabling proactive fault detection and reducing downtime. The CTI module employs transfer-learned representations to detect anomalous network behavior, adversarial probing, and device tampering, integrating with local incident response workflows. We evaluate the framework in simulated and small-scale pilot deployments across three representative clinical settings, demonstrating improvements in fault detection accuracy (average +18%), reduction in unscheduled downtime (median −27%), and early cyber intrusion detection (false positive rate reduced by 22%) compared to baseline local models. The paper discusses practical implementation considerations, governance and privacy-preserving mechanisms, and a roadmap for scalable adoption in resource-constrained health systems.
AI Horizons in Healthcare: Deep Transfer Learning–Enhanced Quality Assurance and Maintenance for Data-Scarce, Cyber-Threatened Environments
Healthcare systems in many regions face a dual challenge: sparse, heterogenous clinical data and an escalating cyber threat landscape that undermines availability and trust. This paper proposes a practical, deployable framework that combines deep transfer learning for quality assurance (QA) with an integrated maintenance system (IMS) to modernize healthcare delivery in such constrained environments. The deep transfer learning component leverages pre-trained models from related domains (medical imaging, physiological signal analysis, and electronic health record patterns) and adapts them via fine-tuning and domain adaptation techniques to perform QA tasks — including anomaly detection, data completeness checks, semantic validation, and provenance inference — under severe data scarcity. The IMS couples predictive maintenance for edge devices, models, and software stacks with continuous security posture assessment, automated patch orchestration, and resilient fallback strategies. We evaluate the framework conceptually using representative case studies (rural diagnostic centers, small clinics with legacy devices, and emergency response networks) and present a mixed-methods results-and-discussion that synthesizes expected improvements in diagnostic consistency, model robustness, and operational uptime. The approach emphasizes privacy-preserving transfer, lightweight model footprints, and security-by-design, enabling accelerated modernization with minimal disruption and risk.
AI-Driven Healthcare Modernization in Data-Scarce Regions: A Quality-Assured Integrated Maintenance Management System with Deep Transfer Learning and Cybersecurity Threat Intelligence
Low-resource and data-scarce regions face persistent challenges: aged/poorly maintained biomedical equipment, scarce labeled clinical data, weak cybersecurity posture, and limited local engineering capacity. This paper proposes an integrated, quality-assured maintenance management ecosystem for healthcare facilities in such regions that combines (1) a lightweight Computerized Maintenance Management System (CMMS) augmented with IoT telemetry and prioritized workflows, (2) deep transfer learning pipelines to enable high-performance predictive maintenance and clinical decision support under extreme data scarcity, and (3) an embedded cyber threat intelligence (CTI) layer to protect patient data and operational continuity. The architecture emphasizes modularity, human-centered design, and continuous quality assurance (QA) cycles to match local constraints (intermittent connectivity, low compute, limited staff). We evaluate expected benefits qualitatively and via simulated experiments on publicly available, small-sample datasets and maintenance logs (transfer learning fine-tuning experiments and RUL estimation simulations) and show improved predictive accuracy versus training-from-scratch approaches and measurable reductions in downtime risk when maintenance is prioritized with ML-driven scheduling. The system also reduces common cyber risks by integrating CTI feeds, anomaly detection, and operational playbooks. We conclude with deployment guidance, limitations, and a research roadmap for field trials and policy alignment.
A Risk-Aware AI Governance Framework for Rural Clinics and Cloud Financial Systems: Cybersecurity Strengthening and Autonomous Threat Detection in DevOps Environments
This paper proposes a unified, risk-aware AI governance framework tailored for two critical yet contrasting domains—rural healthcare clinics and cloud-based financial systems. As both sectors increasingly adopt AI-driven workflows, they face heightened exposure to cyber threats, data integrity risks, and operational vulnerabilities. The framework integrates principles of responsible AI, domain-specific regulatory requirements, and continuous assurance practices into a DevOps and MLOps pipeline. It emphasizes adaptive risk scoring, explainability standards, privacy preservation, and compliance-aligned monitoring to safeguard patient and financial data. A multilayer cybersecurity model is outlined, incorporating autonomous threat detection, anomaly-aware observability, and automated incident-response orchestration. The proposed approach demonstrates how resource-constrained rural clinics and high-availability financial cloud systems can leverage shared governance controls while implementing context-appropriate hardening measures. By embedding risk-aware AI governance into the full software and model lifecycle, the framework aims to improve resilience, transparency, and trust across diverse operational environments.
Next-Gen Intelligent AI Cloud for Fraud Detection and Cybersecurity Defense: Time-Optimized ML Architectures with Deep RiskPredict Intelligence
The rapid rise of digital transactions, multi-tenant cloud platforms, and evolving cyber threats has intensified the need for intelligent, scalable, and real-time security frameworks. This paper introduces a Next-Generation Intelligent AI Cloud Framework that unifies fraud detection and cybersecurity defense through time-optimized machine learning architectures and Deep RiskPredict Intelligence. The framework leverages cloud-native data pipelines to integrate heterogeneous streaming data—including transactional logs, behavioral signals, network telemetry, and contextual metadata—enabling continuous monitoring and rapid incident response in large-scale environments. Central to the design is a suite of time-optimized ML models and deep learning–based RiskPredict engines that balance computational efficiency with predictive accuracy, making the framework suitable for latency-sensitive and resource-constrained operational settings. The RiskPredict module incorporates deep neural networks, multivariate feature interactions, and adaptive risk scoring mechanisms to identify emerging fraud patterns and cybersecurity threats in real time. Empirical evaluation demonstrates significant reductions in detection latency, improvements in fraud and threat classification accuracy, and enhanced system efficiency compared to conventional ML and rule-based approaches. The proposed framework establishes an advanced, cloud-ready security model capable of evolving with complex threat landscapes while supporting proactive defense and financial risk intelligence. It contributes a robust foundation for next-generation AI-driven security systems deployed across diverse and high-demand cloud ecosystems.
Secure DevOps in Cloud-Native Systems: Integrating Cyber Intelligence, Blockchain, and AI for Zero-Trust Enterprise Applications
In the era of cloud‑native architectures, microservices, containers, and continuous deployment pipelines, enterprise applications face a growing challenge: securing dynamic, distributed, and often multi‑cloud infrastructure against rapidly evolving threat vectors. Traditional perimeter‑based defenses are inadequate, as they assume static, trusted boundaries that no longer exist. This paper investigates the integration of Cyber Intelligence, Blockchain, and Artificial Intelligence (AI) within a Zero‑Trust DevSecOps framework to secure cloud‑native enterprise applications. The objectives are: (i) to design an architecture that embeds zero‑trust principles into the DevSecOps lifecycle; (ii) to incorporate cyber intelligence (threat intelligence, anomaly detection) and blockchain (for tamper‑proof logs / identity / trust mechanisms); (iii) to apply AI/ML to both predictive threat detection and adaptive policy enforcement; (iv) to evaluate the effectiveness of this integrated framework via metrics on security, performance, compliance, and overhead. Methodologically, a prototype framework is developed incorporating blockchain for immutable audit trails and decentralized identity management; AI modules for behavior‑based anomaly detection and policy automation; and threat intelligence feeds to inform policies. The framework is applied to cloud‑native applications using containers (e.g., Kubernetes), microservices, and multi‑tenant CI/CD pipelines. Simulated attacks (e.g., supply‑chain attacks, unauthorized lateral movement, credential misuse, container escape) are executed, and the system’s responses are compared to baseline DevSecOps pipelines without the integrated zero‑trust + blockchain + AI additions. The results indicate substantial improvements: detection of anomalous behavior with high recall (~ 90‑95%) and precision (~ 88‑92%); significant reduction in mean time to detect (MTTD) and mean time to respond (MTTR) threats; audit trail integrity ensures non‑repudiation; compliance with zero‑trust policies enforces least privilege and microsegmentation with acceptable performance overhead (~ 10‑20%) in latency. Trade‑offs include increased complexity, computational overhead for blockchain consensus / storage, additional resource consumption for AI inference, need for skill sets, and potential latency / scaling bottlenecks. <span style="font-size: 10.0pt; line-height: 115%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; color: black; mso-themecolor: text1; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">In conclusion, integrating cyber intelligence, blockchain, and AI under a zero‑trust DevSecOps framework offers strong promise for improving security posture of cloud‑native enterprise applications. The study highlights best practices, key trade‑offs, and a road map for organizations wishing to adopt such architectures. Future work should consider scaling to large‑scale multi‑cloud production environments, improvements in blockchain scalability, privacy in AI components, explainability, regulatory compliance, and usability for developers.
Copyright (c) 2025 Bankim Chandra Chattopadhyay (Author) https://creativecommons.org/licenses/by/4.0
AI and IoT-Integrated Smart Agriculture: Predictive Crop Health Monitoring and Sustainable Resource Optimization using Cloud Platforms
Agriculture faces increasing pressures from climate change, population growth, environmental degradation, and the depletion of natural resources. To meet rising food demand sustainably, modern farming must become more datadriven, resilient, efficient, and adaptive. Smart agriculture, integrating Internet of Things (IoT) sensors, cloud platforms, and artificial intelligence (AI), offers promise for realtime monitoring of crop health, early disease and pest detection, precise irrigation and fertilizer control, and overall resource optimization. This paper proposes a comprehensive framework combining IoT sensors deployed in the field (soil moisture, temperature, humidity, spectral imaging, leaf health), AI models in the cloud for predictive crop health monitoring (disease, pest, nutrient deficiency), yield forecasting, and resource optimization (water, fertilizer, energy). The architecture also includes feedback loops to send recommendations to farmers via mobile/web dashboards, actuators for irrigation/fertilization control, and alerting in case of anomalies. We conduct both simulation experiments and a pilot field deployment. Key evaluation metrics include disease/pest detection accuracy, forecasting error (for yield and stress), water usage reduction, fertilizer use reduction, timeliness of detection (latency), system reliability, and user acceptability. Our results show that predictive crop health monitoring with AI achieves detection accuracies of ~9098% for common diseases/pests; yield forecasting errors (MAE/RMSE) are reduced by ~2540% compared with baseline statistical methods; water usage for irrigation is reduced by ~3045%, fertilizer usage by ~2035% with maintained or increased yield; early alerts allow interventions that cut disease spread significantly; cloudbased analytics enable scale, remote access, and integration of multisource data (sensor + imaging + weather). Tradeoffs include issues of data privacy, connectivity and latency, dependency on sensor quality, cloud costs, robustness to environmental variability, and fairness in access for smallholder farmers. The study discusses how to mitigate these via edgecloud hybrid architectures, lightweight models, transfer learning, data augmentation, local capacity building, and flexible hardware. We also explore socioeconomic, policy, and regulatory challenges. In conclusion, AIIoT smart agriculture systems on cloud platforms can significantly improve crop health, reduce resource consumption, and support sustainable farming—especially when carefully designed for environmental, infrastructural, and socioeconomic constraints. Future work will include larger scale deployments, extension to multispectral / hyperspectral sensing, robust models for new diseases, better user interfaces, and exploring federated/cloud hybrid or edgeAI solutions to reduce dependence on connectivity.
Smart Agriculture, Internet of Things (IoT), Cloud Computing, Artificial Intelligence / Machine Learning, Crop Health Monitoring, Disease / Pest Detection, Predictive Yield Forecasting, Resource Optimization (Water, Fertilizer), RealTime Monitoring, Sustainable Farming.
Data-Driven Decision Intelligence: Leveraging AI and Cloud ERP Analytics for Strategic Enterprise Agility and Risk Governance
Organizations are increasingly operating in volatile, uncertain, complex, and ambiguous (VUCA) business environments. To remain competitive, they must be agile, make fast & accurate strategic decisions, and effectively govern risk. Cloudbased ERP systems hold vast operational, financial, supply chain, and human resource data. Coupled with Artificial Intelligence (AI) and advanced analytics, these data assets can enable decision intelligence: the ability to anticipate trends, simulate scenarios, and guide strategic action. However, realizing this promise requires overcoming significant challenges: data silos, data quality, latency, model trust, risk of bias, regulatory compliance, and alignment with governance frameworks. In this paper, we propose a comprehensive framework titled AIDriven Decision Intelligence for Cloud ERP Analytics aimed at enabling enterprise agility and strengthened risk governance. The framework integrates modules for realtime analytics, predictive forecasting, scenario modeling, prescriptive recommendations, and continuous risk assessment. It builds on cloud ERP to provide integrated dashboards, anomaly detection, stress test simulations, and policy compliance modules. Key features include data ingestion and cleansing pipelines; AI/ML models for demand forecasting, cash flow, supply chain disruption, fraud detection; management of model governance (explainability, fairness, version control); and embedding risk governance and audit layers consistent with regulatory and enterprise risk frameworks. We evaluate the framework in a case study of a midsized manufacturing enterprise using a cloud ERP deployment, across modules: supply chain, finance, and operations. Performance metrics include forecasting accuracy (e.g. demand, cash flow), speed of decisioncycle, reduction in risk incidents (inventory stockouts, supply disruptions, financial anomalies), and agility measures (time to respond to disruptions). Results show that predictive forecasting improved demand forecast accuracy by ~2530%, supply chain disruption alerts reduced lead time to respond by ~40%, and risk incidents in finance dropped by ~20%. The agility of planning cycles improved, enabling scenario simulations that allowed leadership to test “whatif” supply or demand shocks. Model governance mechanisms helped improve trust among stakeholders; results also highlight tradeoffs: more aggressive forecasts can generate false alarms; cleaning data and ensuring data quality impose overhead; regulatory compliance may slow down deployment of certain analytics. We discuss the advantages of the framework: strategic alignment, risk mitigation, faster decisionmaking, improved resilience, and competitive responsiveness. Disadvantages include increased complexity, need for skilled personnel, cost of setting up data pipelines and governance, and risk of overreliance on models that may not generalize in novel conditions. In conclusion, AIdriven decision intelligence via cloud ERP analytics offers promising capacity for enterprise agility and robust risk governance. Future work includes refining explainability, integrating external data (market, geopolitical, climate), realtime simulation and digital twin integration; exploring transferability to different industries; and establishing standardized benchmarks for decision intelligence performance and risk governance in cloud ERP settings.
Decision Intelligence; Cloud ERP Analytics; Strategic Agility; Risk Governance; Predictive Analytics; Prescriptive Modeling; Model Governance; Scenario Simulation; Enterprise Data Quality; Business Intelligence.
AI-Driven Predictive Maintenance and Energy-Efficient Robotics for Adaptive Production Systems
Adaptive production systems—characterized by dynamic product variety, fluctuating demand, and complex multi‐stage processes—face constant challenges in maintaining high uptime, minimized energy consumption, and fast response to failures. Traditional maintenance strategies (reactive or scheduled) often lead to machine downtime, suboptimal energy usage, and high operational costs. In this paper, we propose an integrated framework combining AIdriven predictive maintenance with energyefficient robotics to enable adaptive production systems that are resilient, economical, and sustainable. The framework uses data from IoT sensors, historical maintenance logs, realtime condition monitoring, and robotics control signals. AI models—especially deep learning (e.g. LSTM, CNN), anomaly detection, and reinforcement learning—are used to predict component failures (remaining useful life, fault modes), schedule maintenance proactively, and adjust robotic trajectories / power usage to optimize energy efficiency without compromising production throughput. Key components of the framework include: (i) sensor fusion for condition monitoring (vibrations, temperature, current, force), (ii) predictive modeling for failure modes and remaining useful life (RUL), (iii) robotics energy optimization through trajectory planning, idle power reduction, and adaptive motion control, (iv) an adaptive scheduling module that balances production demands, maintenance windows, and energy constraints. The framework is evaluated empirically on benchmark datasets and real robotic cell testbeds covering different robot types (articulated, SCARA) and production tasks. Results show that predictive maintenance can reduce unexpected downtime by up to 3050%, while energy‐efficient robotic strategies can reduce energy consumption in robotic operations by 2035%, with minimal impact (< 5%) on throughput. Further, adaptive scheduling that integrates maintenance prediction and energy awareness yields an overall improvement in system efficiency and reduces operational cost. We also examine tradeoffs: more aggressive predictions may incur false positives (leading to unnecessary maintenance), energy savings may conflict with speed or precision, and the computational overhead of AI/ML models may affect latency. In discussion we consider the energy cost of sensing, data transfer and model inference, the robustness of models under changing operational conditions, and the challenges in deploying in real industrial environments (data quality, maintenance of sensors, integration with existing control systems). The paper concludes that combining AIbased predictive maintenance with energyefficient robotics offers promising benefits for adaptive production systems, particularly under Industry 4.0 settings, but success depends on careful design of sensing/monitoring infrastructure, balancing of performance vs energy vs cost, and robust validation in situ.
Predictive Maintenance, EnergyEfficient Robotics, Adaptive Production Systems, IoT / Condition Monitoring, Remaining Useful Life (RUL) Prediction, Anomaly Detection, Reinforcement Learning, Trajectory Optimization, Energy Consumption Reduction, Industry 4.0 / Smart Manufacturing
Digital Twin-Enabled Smart Construction: Integrating BIM, IoT, and AI for Predictive Infrastructure Maintenance and Sustainability
The increasing scale, complexity, and aging of infrastructure, coupled with heightened sustainability demands, have placed pressure on traditional maintenance and asset management practices. Digital Twin (DT) technology—virtual replicas of physical assets updated in real time—offers promise for transforming infrastructure maintenance from reactive to predictive, thereby improving performance, reducing costs, and promoting environmental sustainability. This study explores how integration of Building Information Modeling (BIM), Internet of Things (IoT) sensor networks, and Artificial Intelligence (AI) methods can underpin a DTenabled smart construction system for predictive maintenance and sustainable infrastructure. The research develops a conceptual framework and implements a pilot case study in a midsized transportation infrastructure (road bridge + drainage network) to evaluate performance. Key objectives are to: (i) define the architecture for integrating BIM, IoT, and AI into a DT system; (ii) develop predictive models for remaining useful life (RUL) of structural components; (iii) assess sustainability gains (energy, materials, emissions reduction) from optimized maintenance; (iv) identify barriers and tradeoffs. Methodologically, the study uses BIM models as baseline asbuilt digital representations, deploys IoT sensors for structural health monitoring (strain, vibration, humidity, corrosion), streams data into a cloud/edge platform, and applies AI/ML techniques (e.g., LSTM, anomaly detection, regression) to predict component deterioration and schedule maintenance proactively. The pilot yields reductions in unplanned failures by ~4560%, maintenance costs by ~2030%, and operations downtime by ~35%, compared to benchmark reactive maintenance. Sustainability metrics show material waste reduction of ~25%, energy use lowered by ~15%, and greenhousegas emissions by ~10% over a simulated 5year horizon. Advantages observed include improved asset life, better resource allocation, and decision support for infrastructure owners. Key disadvantages include high upfront costs, data quality/integration challenges, need for specialized skills, and privacy/security concerns. The results suggest that DTenabled systems can substantially improve predictive maintenance and sustainability outcomes for infrastructure, particularly when BIM, IoT, and AI are well coordinated. For broader adoption, standardization of data models, advanced interoperability, secure data handling, and costeffective deployment strategies are essential. Future work should focus on scaling to larger systems, integrating lifecycle sustainability valuation, automating model updating, and leveraging federated learning for privacy.
Digital Twin, Building Information Modeling (BIM), Internet of Things (IoT), Artificial Intelligence (AI), Predictive Maintenance, Infrastructure Sustainability, Structural Health Monitoring, Data Integration, Lifecycle Management, Resource Efficiency
AI-Assisted Power Management in Renewable Grids: DC– DC Converter Optimization and Predictive Load Balancing for Sustainable Energy Systems
The transition to sustainable energy systems is accelerating, driven by concerns over climate change, energy security, and the need for decarbonization. Renewable energy sources (RES) such as solar and wind are intermittent and variable, posing considerable challenges for grid stability, efficient power conversion, and load and supply balancing. In particular, DC‐DC converters are critical components in renewable energy integration, enabling efficient voltage regulation, maximum power point tracking (MPPT), and interfacing between generation, storage, and load. However, conventional control approaches (fixed‐duty cycle, simple MPPT algorithms, PI/PID controllers) struggle under large fluctuations in irradiance, temperature, load, and energy demand. Predictive load balancing and AI‐based control promise to improve efficiency, stability, and reliability in renewable grids. This paper proposes an integrated framework for AIassisted power management in renewable grids, focusing on two main aspects: (i) optimization of DCDC converters (topology, control parameters, MPPT, voltage regulation) using AI (neural networks, predictive control, supervised & possibly reinforcement learning); (ii) predictive load balancing using forecasting methods for demand, supply, and storage behaviour to schedule loads, manage storage charging/discharging, and reduce mismatches. The framework includes real‐time monitoring of generation sources (solar, wind, etc.), storage state, load demands; forecasting modules; AI controller modules for converter control; and a load scheduler/balancer that offloads, shifts or sheds loads as needed. We conduct both simulation studies and a small hardware/prototype validation. Key performance metrics include energy conversion efficiency, MPPT tracking accuracy, ramp response, stability of DC bus voltage, reduction in power losses, and ability to meet load demands under variable conditions. For load balancing we measure forecast accuracy, demand‐supply mismatch reduction, peak load shaving, and storage utilization. Our results indicate that AI‐based MPPT and converter optimization can increase energy conversion efficiency by ~1020% over conventional MPPT/PI control in typical PV + storage systems under variable irradiance and temperature. Predictive load balancing reduces the mismatch between supply and load by up to 3040%, reduces peak demand by ~25%, and improves utilization of battery storage, leading to smoother operation. The combined approach yields a more stable DC‐bus voltage, fewer voltage dips and transients under load changes, and better overall grid reliability. We also report on trade‐offs: the AI modules introduce computational overhead and may require higher cost hardware; forecasting errors can degrade performance; overfitting or model drift in AI controllers under new environmental conditions. Nonetheless, we conclude that AIassisted DCDC converter optimization + predictive load balancing is a promising route toward sustainable, efficient, and resilient renewable grids.
Renewable energy systems, DCDC converters, Maximum Power Point Tracking (MPPT), Neural networks / Machine learning, Predictive load balancing, Forecasting, Energy storage, Grid efficiency, Sustainable power management, Power electronics optimization
5G-Driven Smart Sensor Networks: Integrating Software- Defined Radios and AI-Based Spectrum Optimization for IoT Security
The rapid expansion of Internet of Things (IoT) applications—ranging from environmental monitoring, industrial automation, healthcare, to smart cities—places ever greater demand on wireless spectrum, low latency, high reliability and strong security. 5G networks promise to deliver on high throughput, low latency, massive device connectivity, and with network slicing, ultra‐reliable lowlatency communication (URLLC) and massive machinetype communications (mMTC). However, effective and secure operation of largescale IoT sensor networks under 5G requires more than just raw capacity—it requires flexible, adaptive radio front ends (such as SoftwareDefined Radios, SDRs), and intelligent spectrum management to optimize utilization and mitigate interference, jamming, and malicious attacks. This paper proposes an integrated framework combining SDR platforms with AIbased spectrum optimization (including spectrum sensing, spectrum sharing, dynamic spectrum access) to enhance both performance and security in 5Gdriven smart sensor networks. We design a prototype system in which sensor nodes are equipped (or interfaced) with SDR modules capable of flexible adaptation of frequencies, modulation schemes, power, etc. On the AI side, we employ a two‐layer model: (i) a local spectrum sensing and anomaly detection module using machine learning to detect spectrum holes, interference, or suspicious behaviour; (ii) a centralized optimization module using reinforcement learning (or metaheuristic algorithms) to allocate spectrum, adjust radio parameters, schedule sensor transmissions, and manage spectrum sharing among nodes and primary users. We further introduce security mechanisms to address threats such as primary user emulation, jamming, false spectrum sensing reports, and unauthorized access. To validate the proposed framework, we simulate a scenario of dense IoT sensor deployment under a 5G infrastructure, with dynamic traffic and potential malicious nodes. Key metrics evaluated include spectrum utilization efficiency, throughput, latency, energy consumption, detection accuracy of spectrum anomalies and attack resilience. Preliminary results show that with AIbased spectrum optimization, spectrum utilization improves by up to ~35–50% (depending on scenario) over baseline static allocation; detection accuracy of attacks rises to ~90–97%; latency and energy overhead remain acceptable (<10–15% overhead) relative to traditional nonadaptive systems. This integrated SDR + AI approach offers a promising direction toward delivering secure, efficient, and resilient IoT sensor networks under 5G. However, challenges remain: complexity of SDR hardware, computational overhead for AI in resource constrained nodes, data privacy when collecting spectrum / usage data, and deploying in real world under regulatory constraints. We discuss these tradeoffs, and propose future work in lightweight AI models, distributed optimization, and testbed and field deployments.
5G, Internet of Things (IoT), SoftwareDefined Radio (SDR), Spectrum Optimization, Spectrum Sensing, Dynamic Spectrum Access, AI / Machine Learning / Reinforcement Learning,mIoT Security, Anomaly Detection, Jamming & Primary User Emulation
Federated Generative AI Framework for Privacy-Preserving Cloud Computing and Edge-Oriented Data Governance
Generative AI methods—such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and hybrid generative models—have shown strong potential in synthesizing data, augmenting sparse datasets, and enabling representation learning. Yet, their deployment in sensitive and distributed environments (e.g. medical, IoT, finance) is constrained by data privacy, regulatory demands, and governance policies. Traditional centralized training requires aggregating raw data in cloud servers, which introduces risks of data leakage, noncompliance with data sovereignty regulations, and trust issues. To address these concerns, this paper proposes a Federated Generative AI Framework designed to support privacypreserving cloud–edge computing with edgeoriented data governance. The framework enables generative model training in a distributed fashion: edge devices locally hold raw data and train subcomponents; edge aggregators coordinate among sets of devices; cloud orchestrators align global models and latent priors. Privacy is protected via multiple mechanisms: secure aggregation (masking, homomorphic encryption or secret sharing), differential privacy applied at local update or latent level, and protocol designs that allow auditability and governance without exposing raw data. Key contributions include: A multitier architecture (device ↔ edge aggregator ↔ cloud coordinator) that supports hierarchical model decomposition and reduces communication overhead. Integration of nonIID adaptation mechanisms: clustering devices based on data distribution similarity; adaptive weighting of updates; generative distillation and latent alignment to reduce divergence among models across nodes. Governance support: local policy enforcement, privacy budget tracking, audit trails, optional use of distributed ledger / blockchain for transparency. Evaluation on benchmark datasets (medical imaging, sensor/IoT timeseries) showing that the federated generative models can produce synthetic data with quality (measured via FID, MMD, downstream task accuracy) close to centralized baselines; that privacy leakage via membership inference or inversion attacks is substantially reduced; that communication and computational overheads are manageable with appropriate choices of cryptographic scheme and aggregation strategy. Our experiments demonstrate tradeoffs: stronger privacy (smaller ε in differential privacy, more masking/encryption) tends to degrade generative fidelity; nonIID settings make convergence slower unless adaptation modules are used. Nonetheless, the proposed framework offers a viable path for deploying generative AI in cloud–edge scenarios where privacy, governance, and regulatory compliance are mandatory. We conclude with a discussion of limitations—particularly on resource constrained devices and adversarial threats—and sketch future directions, including more efficient cryptographic techniques, adaptive privacy policies, and benchmark standardization.
Federated generative models; privacypreserving AI; cloudedge architecture; data governance; secure aggregation; differential privacy; nonIID adaptation; generative distillation.
Light-Matter Interaction at the Nanoscale: From Rayleigh Scattering to Mie Resonance
Light–matter interaction at the nanoscale underpins many emerging technologies such as nanosensors, photovoltaic devices, and nanophotonics. At subwavelength scales, traditional scattering models transition from simple Rayleigh scattering to complex Mie resonances, dramatically altering optical responses. This research systematically explores this transition, establishing unified analytical models, numerical simulations, and experimental validations. Beginning with Rayleigh's r⁶‑scaling with particle radius in the dipole limit, we expand into Mie theory for spherical nanoparticles of varying sizes and refractive indices. By deriving resonance conditions for electric and magnetic multipole modes, we reveal size-dependent optical cross-sections and resonance tunability. Simulated field distributions using finite‑difference time‑domain (FDTD) and discrete dipole approximation (DDA) methods reveal plasmonic hot spots in metallic particles and high‑Q dielectric resonances sensitive to shape perturbations. Experimental measurements on colloidal gold and silicon nanoparticles validated the predicted scattering spectra and near‑field enhancements. Our findings indicate that carefully engineered nanospheres can transition from Rayleigh‑like broadband scatterers to Mie‑resonant elements with narrowband, directionally selective responses. These insights enable optimization strategies for enhanced light harvesting, sensing, and nanoscale energy conversion. This work offers a comprehensive understanding of the fundamental optical behavior of subwavelength particles, guiding design principles for next-generation nanophotonic devices.
Rayleigh scattering, Mie resonance, nanoparticles,
finite difference time domain, discrete dipole approximation,
dielectric resonators, optical cross-section, near-field enhancement
Development of Efficient Algorithms for Multiple Sequence Alignment of Large Genomes
Multiple sequence alignment (MSA) of large genomes is a cornerstone in comparative genomics, evolutionary biology, and functional annotation. However, as genome sizes and numbers increase, existing MSA tools often become computationally prohibitive in terms of time and memory. This work presents novel algorithmic strategies and data structures that enhance both scalability and accuracy for large-genome MSA. We introduce a hybrid divide‑and‑conquer framework that partitions genomes into syntenic blocks using efficient anchoring heuristics, followed by localized alignment refinement leveraging profile‑profile Hidden Markov Models (HMMs). Spatial indexing with sparse suffix arrays and wavelet trees enables fast seed discovery and scalable representation of alignment profiles, reducing memory overhead from O(N × G) to O(N + G). Parallelization via map‑reduce style workflows ensures near‑linear speed‑up across distributed computing environments. Evaluated on simulated and real-world datasets—including human, mouse, and plant genomes totalling over 10 Gb—our algorithms achieve 3–5× faster runtimes and up to 60% less memory usage compared to leading tools like MAFFT, Clustal Omega, and Cactus, while maintaining or improving alignment accuracy by 5–8% in standard metrics (SP‑score, TC‑score). We also demonstrate application in pan-genome analysis of Arabidopsis thaliana accessions and structural variant detection in mammalian genomes. Our approach makes feasible large-scale comparative analyses previously limited to small genomes or partial synteny. These innovations chart a path toward efficient and accurate genomic alignments in the era of ever-growing sequencing data.
divide-and-conquer, syntenic blocks, profile HMM,
sparse suffix arrays, wavelet trees, distributed computing,