American Advanced Journal for Emerging Disciplinaries (AAJED)
Journal Summary The American Advanced Journal for Emerging Disciplinaries (AAJED) is an international, peer-reviewed, open-access scholarly journal established to advance high-impact research across emerging, interdisciplinary, and rapidly evolving fields of science, technology, engineering, and society. The journal serves as a premier academic platform for researchers, scientists, engineers, academicians, practitioners, educators, and policymakers from around the world to disseminate original and innovative findings. AAJED is committed to publishing high-quality research that addresses real-world challenges through innovative methodologies, evidence-based solutions, and emerging technologies. The journal encourages interdisciplinary research and welcomes contributions that combine theoretical advancements with practical applications across diverse domains including science, engineering, technology, artificial intelligence, data science, health sciences, environmental studies, social sciences, education, management, and economics. All manuscripts submitted to AAJED undergo a strict double-blind peer-review process conducted by qualified experts in the relevant field. This ensures that every published article meets the highest standards of scientific rigor, originality, and academic excellence. Published using the Open Journal Systems (OJS) platform, AAJED follows internationally recognized publishing standards and is dedicated to transparency, research ethics, and open knowledge sharing. The journal assigns Digital Object Identifiers (DOIs) to all published articles, ensuring permanent accessibility and global discoverability. AAJED is indexed in Scopus (Elsevier), Web of Science (Clarivate), DOAJ, CrossRef, and other major academic databases and directories, providing maximum visibility and citation impact to published research. Authors benefit from a streamlined submission process, prompt peer review, and rapid publication timelines. With a growing international readership and a diverse editorial board comprising leading experts from reputed institutions worldwide, AAJED continues to strengthen its position as a trusted and respected journal in the global academic community. Quick Facts Detail Information Journal Title American Advanced Journal for Emerging Disciplinaries Short Title AAJED ISSN 3067-2716 Journal Type Peer-Reviewed, Open Access Review Process Double-Blind Peer Review Publication Frequency Quarterly (4 Issues per Year) Language English Platform Open Journal Systems (OJS) Website ajeed.org Subject Area Science, Engineering, Technology, AI, Data Science, Health Sciences, Social Sciences, Education, Management, Economics and Emerging Interdisciplinary Fields
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Building Secure and Scalable AI Intelligence for the Modern Enterprise
Context-aware enterprise intelligence (C*EI) integrates business intelligence, data mining, and predictive analytics in complex networked systems. The demand for immediate decision support and automation necessitates constant engagement with data systems attuned to the context of use. AI augmentation promotes intelligent products and services, further heightening reliance on corporate data systems. However, while C*EI is essential to maximize the value of trade, business systems often fail under the dual burdens of security and scale. This research defines C*EI, identifies core requirements for enterprise data systems, and proposes a secure and scalable architecture to orchestrate an AI-augmented data ecosystem. Supporting design principles address security by design, modular scalability, and contextual data modeling. Hypothetical use-case scenarios demonstrate the architecture’s potential. The findings provide practical guidance for organizations seeking to implement context-aware, AI-augmented enterprise data systems while safeguarding assets from adversarial attack and supporting rapid scaling in response to load. Context-aware enterprise intelligence (C*EI) integrates business intelligence, data mining, and predictive analytics in complex networked systems. The demand for immediate decision support and automation necessitates constant engagement with data systems attuned to the context of use. AI augmentation promotes intelligent products and services, further heightening reliance on corporate data systems. However, while C*EI is essential to maximize the value of trade, business systems often fail under the dual burdens of security and scale. This research defines C*EI, identifies core requirements for enterprise data systems, and proposes a secure and scalable architecture to orchestrate an AI-augmented data ecosystem. Supporting design principles address security by design, modular scalability, and contextual data modeling. Hypothetical use-case scenarios demonstrate the architecture’s potential. The findings provide practical guidance for organizations seeking to implement context-aware, AI-augmented enterprise data systems while safeguarding assets from adversarial attack and supporting rapid scaling in response to load.
Copyright (c) 2025 Yannis Ioannidis (Author) https://creativecommons.org/licenses/by/4.0
Responsible AI Adoption in Government Tax Compliance
Artificial intelligence (AI), mixed with advanced big data analytics methods, has the potential to play a pivotal role in enhancing efficiency and accuracy in multiple tax processes, from data management and fraud detection to pricing optimisation and performance assessments. However, the use of these new technologies must respect the ethical principles of transparency, accountability, and explainability in order to secure their acceptance by both internal and external stakeholders, ensure compliance with increasingly stringent financial regulations and well-founded tax decisions, and ultimately facilitate a more equitable tax regime. A Responsible AI framework is articulated for an area typically overlooked in the discussion of the ethical use of AI in other fields: its application in government analytics and compliance systems for tax agencies, natural and legal persons subjected to taxation, and internationally coordinated data-sharing agreements. These institutions frequently combine their own datasets with information sourced externally through digital interceptions, companies and organisations mandated to carry out withholdings or disseminate internationally belonging to third parties, data of a fiscal nature of a different nature that guarantees compatibility in the absence of a tax treaty, and other types of financial data. Given the sensitive nature of tax-administrative data and the legal obligations in force for tax agencies, the AI systems developed should respond adequately to the three dimensions of Responsible AI: securing ownership of data and results; avoiding biases during the design stage and in the response stage; and guaranteeing control and security of the systems' outputs.
Copyright (c) 2025 Jens Lehmann (Author) https://creativecommons.org/licenses/by/4.0
Research focusing on tax compliance investigations is often anecdotal. In this study, data from the Thai Revenue Department's electronic tax filing system are used with pattern recognition techniques to examine the compliance behavior of both businesses and individual income tax filers. The empirical investigation seeks to identify patterns of behavior and any other anomalies. Importantly, the investigation is not undertaken to explain causes and consequences of tax compliance but simply to provide a framework for investigation and a database based around a pattern recognition analysis. The analysis reveals clear patterns of compliance behavior within the two data sets, although the data from businesses is more consistent. Anomalies and irregularities are also evident, often recommending further and deeper investigation. Such an approach to tax compliance investigations provides broad and objective insight into typical behavior, while highlighting specific sets of records that require further investigation. Although these insights into the compliance behavior of Thai tax filers are somewhat limited, they nonetheless have potential value for the country's tax administrators. The framework can, however, be deployed in more general research into electronic tax filing compliance and enables the recognition of behavior that is not only anomalous but also worthy of further scrutiny. This enables, and possibly improves, resource allocation by tax authorities seeking to maximize the benefits from compliance investigations while minimizing the costs.
Copyright (c) 2025 Tanja Schultz (Author) https://creativecommons.org/licenses/by/4.0
Clinical Decision Support Using Patient History
Clinical Decision Support systems based on historical patient data represent a model for investigation of a category of Clinical Decision Support (CDS) applications that have recently gained traction — consumption of historical data to support clinical decision making. An overview of the category is provided and addressed based on five research questions: What are the properties of historical data that provide insights? How are these insights used in Analytical Methods for Clinical Decision Support based on Historical Data? How is the architecture of the Clinical Decision Support System represented? How is clinical validation and evaluation achieved? How is the presentation and user interaction with predictions controlled? Despite being a nascent area, numerous published works are available on the topic, and clinical validation efforts are in various stages of maturity. Historical data have become increasingly more available, are generally regarded as safe, and, unlike predictive models constructed on Future Data, do not raise questions regarding biases introduced in the construction of supervised learning models. However, while safety and bias concerns are reduced, historical data are prone to their own logical shortcomings. Therefore, users must exercise caution when interpreting the insights derived from Historical Data Analytics. Properly defined and presented, such insights do not impose a decision making burden on clinicians but instead ease the decision making process. The existence of a predictive model that maps the Clinical Data relevant to a Clinical Decision to the Clinical Decision itself is often regarded as a requirement for CDS to be genuinely useful for everyday clinical practice. Such a statement suffers from circular reasoning. In summary, because no method of construction of predictive models can claim to be free from bias, the pannational nature of probability suggests that, provided sufficient Causally Uncorrelated Data points are available notwithstanding time, space and biological differences, the possibility of identifying and using a Supervised Learning Model that accurately predicts the Decision of a Clinician can not be excluded.
Copyright (c) 2025 Alexander Miller (Author) https://creativecommons.org/licenses/by/4.0
Agentic AI and Big Data Framework for Real-Time Wholesale Food Supply Chain Optimization
Supply chain optimization in the wholesale perishable food sector is complicated by the challenge of minimizing waste and profit loss from missed demand. An agentic, sustainable, and privacy-preserving architecture to ingest private data from distributed members is outlined, which supports the orchestration of decision-making workflows either directly in Cloud frameworks or privately in members’ premises. The agentic orchestration layer integrates an Agent-Based Modelling architecture with event-driven and trigger-action orchestration. Social-Computing protocols enable external agent coordination. The framework uses Big Data modelling and Agents to support six decision-making workflows for real-time optimization of wholesale perishable food supply chains. These workflows forecast total demand from private Big Data events and optimize inventory allocation, sensing, and fulfillment. Path-Dependence Theory, complex adaptive systems, and Human-Computer Interaction design the architecture. Built with Big Data technologies and Polybase integration of SQL Server, Azure SQL Data Warehouse, and Hadoop, the framework seeks to stimulate supply chain member investment through value-sharing sentiment analysis.
Copyright (c) 2024 Alexander Miller (Author) https://creativecommons.org/licenses/by/4.0
Trust-Embedded Agent Orchestration for Enterprise Service Management
With enterprises increasingly adopting generative AI models, there is an escalating need for technology governance. Agentic generative AI promises increased process automation but requires intricate ownership and accountability mechanisms. This architectural design addresses these issues when RAG pipelines automate IT service management (ITSM) or human resource service delivery (HRSD) use cases. It proposes a governance-native agentic AI architecture, embedding governance in every part of the design, from process selection to retrieval-augmented generation (RAG) pipeline specification and technological implementation. Every enterprise’s AI strategy and governance framework should determine their agentic AI governance principles. Here, ownership of the agentic generative AI is repositioned from the enabler to the providing organization’s governance risk compliance team. All internal IT knowledge sources, including policies, process, procedures, service catalogs, and product catalogs, require deep dive ingestion and curation. External knowledge sources, including resumes, job descriptions, and industry-specific suppliers, also need similar preparation. Therefore, three RAG pipelines—management of IT operations incidents and turn-up requests and deploying non-major service change requests—are augmented with a governance layer that enforces audit trails, supervises process usage and model alignments, and drives awareness and training.
Copyright (c) 2024 Benjamin Clark (Author) https://creativecommons.org/licenses/by/4.0
Optimizing Colocation Case Management Globally
Global colocation services provision mission-critical infrastructure in secure and reliable facilities, typically within a zero-tolerance SLA environment. Strength of colocation services is the volume of incidents and service requests, and the case management workflow is a critical determinant of the improvement of the end-to-end response capacity and quality. An objective, evidence-based exploration of the principles of colocation operations case management covering design, metrics, and pathways for continuous enhancement has been conducted. A structured inquiry engaged diverse sources: primary and secondary research, case study analysis following global delivery and service operation models, information provided by subject matter experts, and management performance benchmarks proposing KPIs. The investigation results in the articulation of fundamental principles and frameworks that govern the management of incidents and requests in colocation operations. Evidence of the natural path to optimization supports their formulation. Continuous awareness, evaluation, and reinforcement of the principles and frameworks contribute significantly to performance enhancement.
Adaptive AI for Autonomous Cloud Orchestration
Distributed cloud infrastructures integrate multiple services at the edge, fog, and cloud, posing diverse automation and monitoring challenges. Automation aims to simplify tasks and establish migration, scaling, and recovery rules to minimize manual intervention. Monitoring AIOps enables fault prevention and reduced Mean Time to Recovery (MTTR) while assuring service resilience. Hybrid AI blends machine learning (ML) models with symbolic reasoning and optimization techniques to formulate intelligent solutions for these scenarios. Use of such technology is gaining popularity for AIOps. The growing frequency of severe data breaches urges organizations to adopt a Data Loss Prevention (DLP) strategy to reduce the risk of sensitive data exposure. DLP aims to detect potential data breaches and prevent the unauthorized transfer of those assets. According to the Cybersecurity and Infrastructure Security Agency (CISA), configuring DLP controls can help organizations reduce threat surface visibility. Proper implementation and enforcement of DLP controls also offer organizations the ability to identify regulatory compliance issues early. Furthermore, detecting and mitigating data leakage incidents contribute to organizations' ability to comply with regulations, such as the Health Insurance Portability and Accountability Act (HIPAA), and reduce their impact. Hybrid AI models enable intelligent automation and monitoring of workflows in distributed clouds by facilitating orchestration, supporting recurrent decision logic, providing data-driven insights, and automating response actions. The use of Hybrid AI for workflow automation is evaluated through automated elastic data ingestion pipelines (ADIPs) capable of ingesting streaming sensors data and migrating it to the cloud for storage, post-processing, and consumption. Moreover, an evaluation of AIOps in distributed cloud environments is performed, addressing data leakage prevention and intrusion detection incidents. The proposed solutions leverage different information sources such as network flows, endpoints, databases, and cloud resources and adopt supervised, unsupervised, and knowledge-based methods for detection, analysis, and mitigation. The presented approaches constitute practical use cases that explore benefits and limitations of Hybrid AI and provide guidance for its effective application.
Copyright (c) 2026 Bhasker Katta (Author) https://creativecommons.org/licenses/by/4.0
Predictive Data Systems for Precision Medicine
The paper discusses the critical importance of data ecosystems within cloud-native environments designed for predictive and automated decision-making systems in healthcare. The evolving requirements for healthcare service delivery necessitate an alternative architectural paradigm that goes beyond traditional monolithic systems and monolithic software applications hosted in dedicated private data centers or single public cloud infrastructures. Service-oriented deployments are best modeled using microservices and service meshes, data contracts, and independent deployments. Precision healthcare addresses disease prevention, detection, forecasting, diagnosis, management, and outcome prediction for individuals or specific population sets. It leverages underlying compute, storage, and networking services designed for traditional enterprise workloads with strict recoverability and business continuity requirements. Frameworks for scalability, resilience, and disaster recovery translate elasticity features from the public cloud domain, while supporting cloud-native solutions in hybrid or multi-cloud environments. The success of such an approach is entirely dependent on effective data protection techniques, access controls, and the ability to demonstrate compliance with multiple regulations.
Copyright (c) 2024 Daniel Thompson (Author) https://creativecommons.org/licenses/by/4.0
Secure Multi-Agent RAG for Enterprise Automation
Automating enterprise operations offers the promise of substantially increased agility and competitive advantage. However, enterprises are still constrained in their ability to leverage such innovations for many operations beyond routine transaction processing. Security and oversight requirements ordinarily preclude the deployment of advanced AI techniques in data-rich enterprise context. Recently proposed retrieval-augmented AI designs provide a means to address some of these limitations, enabling formal institutions to enforce policy and mitigate risk, even when operations require complex reasoning and data synthesis. Yet no formal framework for retrieval-augmented multi-agent systems exists. Such a framework is necessary to support comprehensive validation and evaluation of emerging systems while also guiding practical implementations. Providing such an underpinning costitutes the principal contribution of this work. The contributions of this study formalize the design of retrieval-augmented multi-agent systems. A foundation architectural model is defined, linking agent modularity, open interfaces, and scalable operation to retrieval-augmented operation. Grounding the architecture for enterprise contexts supports a comprehensive theory of retrieval-augmented multi-agent systems. Security and governed operation are addressed via consideration of (1) agent role definitions; (2) data access and provenance management; (3) strategic behaviour, information source maintenance, and understandable queries. Together, these aspects provide the procedural scaffolding necessary for scientifically-grounded operational and performance evaluations, endorsing the continued development of novel retrieval-augmented multi-agent systems for enterprise automation.
Copyright (c) 2023 Christopher Wilson (Author) https://creativecommons.org/licenses/by/4.0
Risk Stratification Through Predictive Analytics
Predictive analytics seeks to identify future risks and opportunities for individuals and groups. In a population health risk stratification context, addressing the higher level of risk associated with a subpopulation creates the potential for improved outcomes and reduced costs. The proposed study aims to improve predictive analytics for hospital admission, readmission, and emergency department (ED) visit risk stratification in an insurance network using advanced methodologies. Primary data sources from claims and provider networks provide the foundation for prediction and the design of use cases to support the predictive engine. Sensitivity analyses assess predictive equity along social determinant lines to ensure fairness. Model performance is measured in terms of the receiver operating characteristic, precision-recall, F1 score, area under the precision-recall curve, and Matthews correlation coefficient. Results could influence resource allocation, care management priorities, contracting, and network design. Population health risk stratification identifies clusters of individuals with shared health risk characteristics for prioritizing health care actions and interventions. Care management interventions are generally devoted to individuals in specific need categories—those at risk of hospitalization for specific acute or chronic conditions, recently discharged from the hospital, facing social or economic barriers to achieving good health, receiving end-of-life support, or dealing with multiple medical conditions—and the overall levels and distribution of illness burden and acuity can inform the investment and allocation of resources across these care pathways. Predictive analytics seeks to identify future risks and opportunities for individuals and groups. In a population health context, addressing the higher level of risk associated with a subpopulation creates the potential for improved outcomes and reduced costs.
Copyright (c) 2026 Ganesh Pambala (Author) https://creativecommons.org/licenses/by/4.0
Smart Customer Experience Management for Scalable Data Center Services
Digital service delivery in data centers has matured over the past decade, yet customer experiences (CX) remain at best mediocre. Deep learning (DL) technologies enable CX and business key performance indicator (KPI)-shaping initiatives through customer segment personalization, meaningful service customization, and proactive service enablement. These innovations are anticipated to bring more impactful and tangible improvements than previous use cases in the CX space. Such effort is essential, as noted in a McKinsey report, which concludes that great CX not only brings growth but also creates a mode around the organization. The associated work in service experience management for the cloud managed service space highlights mechanisms driven by DLs that encompass the entire CX space– from enablement to post-support to personalization. Delivering the anticipated improvements in CX at scale is key, as the implementation of traditional “happy paths” enabled by DLs does not contribute to further growth in CX and associated business KPIs. Explore customer experiences that open the floodgates at scale, as evidenced by the use of trains, planes, and healthcare. Leaning bodies have shown that race-car style data-enabled running styles result in less injury and improvement in race performance. The continual ability in airplanes to maintain height at constant velocity through air traffic control–bending the high and low pressures–lowers carbon footprint without passengers noticing. It’s high time the use of this tried and tested normal CX exploration at scale and hence the activation of a single CX at DCS is rolled out wherever applicable.
Cloud-Native Financial Compliance Intelligence
Real-time compliance requirements pose considerable challenges to service-oriented, cloud-based regulatory technology (RegTech) systems. The volume, variety, velocity, and variability of adverse events often necessitate rapid action. High-frequency trading systems, for example, may have to react to changes in government policy markets within milliseconds. Other triggering events might require a risk-based response to data on machine-to-machine fraud at an appropriate decision time horizon. Information security threats demand constant vigilance and prompt countermeasures. While regulatory compliance is primarily enforced via laid-down policies, lifecycle monitoring of the policy-defined controls is often considered neither formal nor critical for compliance. Dynamic policy enforcement, however, renders this choice invalid. Latency requirements for the governance of RegTech systems must therefore extend beyond the immediate-response horizon to include compliance-related data processing, storage, and data quality management. Such requirements are applicable to both the optional compliance checks that stop short of automatic intervention and to the auxiliary non-functional compliance triggers such as privacy or security by design.
Copyright (c) 2026 Vinod Battapothu (Author) https://creativecommons.org/licenses/by/4.0
An Integrated Modernization Architecture for Data-Intensive Industries
An AI-driven architectural framework for cloud-enabled data modernization in business domain was proposed. The framework helps organizations to efficiently endure the cross-sector convergence of cloud computing and Artificial Intelligence by addressing Data modernization on-demand and ensuring high-quality data for Artificial Intelligence algorithms. Data modernization leverages cloud computing in the Software as a Service delivery model, achieves Data product reliability and trustworthiness through Data governance, and meets usage requirements through scalability Data -including Supply, Processing, Storage, and Consumption layers- and Automation. Cross-sectoral deployment was validated by the foundation of the framework in three business domains (Retail, Healthcare, and Finance) and a detailed analysis of requirements. In parallel, the phased adoption of Data modernization was assessed through a maturity model and an Organizational and Team Readiness analysis. Organizations from all business domains are converging towards the cloud, and so are several processes of Artificial Intelligence Data products. These two trends are fundamentally transforming the Data surrounding businesses by fueling Data modernization. Hyperscalers leverage their global network of Data centers to provide on-demand, scalable, and pay-per-use enablement of Data product development and operation. This unprecedented operational agility represents an attractive opportunity for organizations seeking fast response time and cost competitiveness. However, organizations often struggle in Data modernization, and Data products can suffer from inaccurate, incomplete, inconsistent, or outdated Data, thus risking unexpected behavior, poor prediction performance, or wrong decisions. AI-ready Data ecosystems –encompassing Data Supply, Processing, Storage, and Consumption– that ensure quality Data should be made available for low-latency AI-enabled analytics.
Copyright (c) 2023 Dileep Valiki (Author) https://creativecommons.org/licenses/by/4.0
Agentic AI for Cloud-Native Big Data Orchestration in National Food Wholesale Supply Chains
Undertaking empirical research in the food service wholesale domain of national-scale (US) supply chains, this work focuses on agentic AI-enabled orchestration for cloud-native supply-chain optimization. Problems derive from heavyweight supply chains serving capital-­ and land-intensive industries, such as agriculture and forestry, with demand patterns poorly driven by underlying ecosystems yet bolstered by growth dynamics in surrounding food service markets. Discussion is framed around orchestration of big data pipelines for supply-chain analytics and decision support. An extended tenet of adaptive governance empowers business-level stakeholders to integrate data sources and keep their national networks in sync. Contributions articulate core supply-chain requirements and actors’ analytical needs, introduce a methodological framework for designing data-ingestion and orchestration mechanisms, and recommend a reference system architecture that generalizes to other national-scale economies. Spanning compliance, quality, safety, and cybersecurity, these considerations together speak to agentic AI’s accountability, transparency, and risk. The empirical core comprises synthesis case studies feeding into three use cases for cloud-enabled supply-chain dynamics: forecasting end-customer demand, integrating supplier inventories and resources, and optimizing transport. Even with only a few data sources, agentic AI-enhanced decision support promises genuine advantage: helping generate matching supplier networks and synchronizing transport operations not just for cost but also to mitigate product waste, quality loss, and spoilage. A supporting technical framework addresses the cloud-native supply-chain-analytics components of systems for national-scale economies—conducting core supply-chain planning in the food-service wholesale domain, such as forecasting, integrating supplier inventories, and synchronizing transport across capital-­ and land-intensive supply chains that depend on nature’s undulating growth cycle.
Copyright (c) 2026 About the Journal — American Advanced Journal for Emerging Disciplinaries (AAJED)
AI-Powered Risk Discovery in the Data Age
Integration of Big Data Analytics and Artificial Intelligence (AI) technologies is transforming risk assessment practices across industries, including banking, insurance, and investment. Yet, a comprehensive typology of risks considered, corresponding data requirements, and pipeline designs remains elusive. The synthesis offered here supports risk assessment through the structured integration of various Big Data and AI methods throughout the risk management cycle. Current literature is examined to identify risk models spanning credit, market, operational, liquidity, and reputational risk. For each, the critical data sources required to train and evaluate the models are documented. These data can be harvested from within the institution or supplemented with external sources. Emerging developments in risk model performance assessment are also discussed, including the importance of distinguishing between model calibration and discrimination. Finally, state-of-the-art Big Data and AI technologies for risk evaluation are mapped to the corresponding risk classes. Integration of Big Data Analytics and AI technologies is transforming decision-making processes across industries, including banking, insurance, and investment. Such transformation holds potential for long-standing data-hungry tasks, such as fraud detection and customer profiling, which have been shrouded in the secrecy of proprietary models for years. Moreover, support for these daunting processes is becoming increasingly critical given the rising prevalence of new data sources such as social media and market sentiments. Yet, a comprehensive typology of risks considered, corresponding data requirements, and pipeline designs remains elusive. The synthesis offered here supports risk assessment through the structured integration of various Big Data and AI methods throughout the risk management cycle.
Cloud-Native AI Architectures in Modern Agriculture
Precision agriculture is considered one of the key solutions to satisfy world food demand by 2050. To achieve precision at the smallest farming unit, data collected, processed, and analysed to produce fit-for-use knowledge must be visible, sharable, and accessible among all stakeholders. Cloud computing technology on the foreshore has penetrated all segments of everyday life, not sparing the agriculture segment, making visible the nondirectional flow of information in real time. Cloud computing generally provides scalable infrastructure, powerful storage, data-sharing facilities, low operational costs, and powerful analytics, metering AI/ML model development and inference more affordable and enabling artificial intelligence and machine learning development and inference at regional scales. Its challenges include single-point failure, low-quality service, latency, and resource dependency. Conclusively, precision agriculture at the farm and field levels can also benefit from clustering agriculture stakeholders’ data on the cloud and making the data accessible to eminent researchers and agriculture domains using various algorithms to develop AI/ML models. The planned architecture helps shed light on the logical flow of information from the sensor to decision representation. It includes key functional modules, components required for data ingestion, integration towards data lakes, model training nested in the cloud, deployment in different environments for inference, and governing data with respect to modelling, privacy, and security of stakeholders’ data in the cloud. To satisfy world food demand by 2050, precision agriculture is considered one of the key solutions. Precision requires collecting, processing, and analysing data to produce fit-for-use knowledge that is visible, sharable, and accessible among all stakeholders. Cloud-computing technology on the foreshore has penetrated all segments of everyday life, not sparing agriculture, and enabling a non-directional flow of real-time information. Cloud computing generally provides scalable infrastructure, powerful storage, data-sharing facilities, low operational costs, and powerful analytics, making artificial intelligence and machine learning development and inference at regional scales more affordable. The technology’s challenges include single-point failure, low-quality service, latency, and resource dependency. Precision agriculture at the farm and field levels can benefit from clustering agriculture stakeholders’ data on the cloud and making the data accessible to eminent researchers and agriculture domains.
Copyright (c) 2025 About the Journal — American Advanced Journal for Emerging Disciplinaries (AAJED)
The mission of intelligent transportation systems (ITS) is to enhance transportation safety, efficiency, mobility, and sustainability through the integration of data from multiple sources. Despite the celebrated success of artificial intelligence (AI) in development and commercialization, its growing capacities, accessibility, and affordability have not been capitalized on in ITS. This is due partly to the lack of comprehensive and clearly delineated means for infrastructure provision, timeliness, and system continuity—especially in maintenance, operations, and expansion—and partly to the underlying data fundamentals. AI engenders unprecedented opportunities for transportation solutions by providing its own data engineering. Examination of AI-Powered Data Engineering methods reveals the enabling means of AI-based data engineering for ITS. Data engineering encompasses the core functions for the acquisition, preparation, and deployment of data suitable for analysis and modeling. Like the broader IT domain, AI-Powered Data Engineering for ITS operates on a foundation of data acquisition and ingestion; integration and interoperability; architecture; optimization; security; governance; agency; and deployment. The function for data acquisition and ingestion serves the dual purpose of ingesting system-based information—such as from traffic signals and detection cameras used for system administration—as well as supporting predictive models for demand and congestion forecasting conversation systems for route planning and incident mitigation.
Architecting Jurisdiction-Aware Quantum Cloud Ecosystems
Quantum computing is now recognized as the most impactful related technology for the next five years. Manufacturers have made enormous investments, major milestones have been achieved, and the pace of corporate commercialization of quantum technology is accelerating. However, comparatively little attention has been given to the impacts of quantum computing on cloud infrastructure. This is surprising, given the considerable resources made available to corporate research projects in the area, involving leading manufacturers. The enabler of quantum computing, the qubit, is qualitatively different from the classical electronic unit. Hence, for the expected advantages of quantum computing to be realised, new models, architectures and educational schemes for a Quantum Cloud Infrastructure (QCI) are needed. These must take into account the behaviours of qubits, the expected advantages in specific applications, the anticipated hybridisation of quantum-classical infrastructures, and the relevant education, service, deployment and regulatory particulars. In addition to hardware-specific educational requirements for quantum clouds, the dominant questions for users are economic. For which workloads are quantum cloud resources in a commercial context likely to be more competitive than classical? What key performance indicators might indicate that expected advantages are close? The identification of management and resource strategies that possess such characteristics would allow organisations to focus limited resources on workloads where true Quantum Advantage in either Procurement or QA-as-a-Service seems probable.
Architecture and Communication Protocols
Quantum communication and the quantum internet are converging subfields of research based on the application of quantum mechanics to the exchange of information, either to enhance existing protocols or to enable entirely new ones. Milestones in this journey include Bell inequalities, indistinguishability-proof secret-key distribution, uncertainty, and no-cloning; the proof of the quantum channel capacity theorem; and the exponential advantage of quantum teleportation over the classical teleportation of qubits. The quantum internet is fundamentally layered, akin to the classical internet but enabling the orchestration of quantum mechanics in a protocol stack that may span multiple administrations. Achievements to date cover many ground-layer requirements for quantum key distribution and entanglement distribution, enabling basic demonstrations of provably secure secret-key generation between metropolitan quantum networks. A threat model for quantum networks and a thorough discussion on security and privacy emphasize the unique nature of quantum mechanics as a potential asset in devising security protocols. Quantum communication is defined as the exchange of information between physical systems described by quantum mechanics, and entanglement is perhaps the most distinctive aspect of quantum information theory. Quantum channels connect separated quantum systems, allowing the distribution of quantum information over distance. In networked quantum information, a number of parties need not only prepare or measure quantum information but also rely on the successful transmission of quantum information over a quantum network. End-to-end quantum communication protocols supporting specific tasks—quantum key distribution; indistinguishability-proof secret-key distribution; quantum teleportation; state transfer and entanglement swapping in quantum relays; and entanglement distribution—have been formulated and investigated from a physical-layer perspective. Each protocol is examined according to vulnerability to nonideal conditions and realistic operation requirements, enabling comparisons of achievable performance. While advantages of quantum protocols are becoming practical, their exploitation in practice demands careful attention to nonideal performance and the avoidance of unrealistic assumptions.
Safeguarding Digital Sovereignty in the Quantum Era
Digital sovereignty encompasses a nation or region’s control over information and services crucial to the nation state. This involves making essential decisions about security, economic, ethical, and political factors contributing to the provisioning of those services. National digital sovereignty risk being compromised when critical services such as authentication, signing, encryption, or confidentiality are provided by foreign providers. Sovereignty in general can also be considered more broadly at the regional level and include other service attributes, such as resilience, diversity of supply, and quality. The increasing arrival of quantum computers will undermine classical public-key cryptography. Although the development of practical quantum computers is not imminent, governments, regulators, and other owners of critical infrastructure must urgently begin adopting post-quantum key-management solutions as part of a wider digital-sovereignty strategy. Failure to do so will not only compromise the confidentiality and integrity of data exchanged over many years, but also introduce a risk of undetected abuse of those communications during the transition. The increasing arrival of quantum computers will undermine classical public-key cryptography. Although the development of practical quantum computers is not imminent, governments, regulators, and other owners of critical infrastructure must urgently begin adopting post-quantum key-management solutions as part of a wider digital-sovereignty strategy. Failure to do so will not only compromise the confidentiality and integrity of data exchanged over many years, but also introduce a risk of undetected abuse of those communications during the transition.
Copyright (c) 2024 About the Journal — American Advanced Journal for Emerging Disciplinaries (AAJED)
Quantum Threats and the Future of Cyber Defense
Quantum threats to classical and quantum-resilient cryptography and deterrence mechanisms for cyber warfare underpinnings are investigated. The fundamental principles of quantum eavesdropping techniques, quantum computing, and cryptanalytic algorithms, including Shor's and Grover's algorithms, are outlined. The most widely examined post-quantum cryptography primitives—lattice-based, multivariate-quadratic-equations-based, code-based, and hash-based solutions—are compared in terms of their respective capabilities and inherent vulnerabilities. Quantum cyber deterrence concepts—strategic stability, defensive postures, resilience, risk management, capability hardening, and offensive thresholds—are examined. The potential for deterrent or coercive application of quantum computing in digital warfare is analyzed, with an emphasis on ethical, legal, and strategic considerations. The development of internationally accepted certification procedures and compliance frameworks is essential to effectively manage the post-quantum digital transition, providing a foundation for effective risk management and enable the emergence of governance, regulatory, and normative frameworks for responsible and stable cyber operations. The establishment of strategic, cooperative, and operational confidence-building measures, as well as the generation of deterrent cyber-resilience capabilities, is crucial to enabling stable deterrence in digital space—one that supports not only the prevention of cyberwar but its controlled, limited, and responsible conduct when required.
Strategic intelligence remains a foundational aspect of organized governance and effective policies. Empirical analysis and predictive models have long been recognized as promising for improving the forecasting accuracy and reliability of strategic intelligence. Additionally, recent advances in natural language processing and generative adversarial models provide exciting opportunities for developing innovative fusion models that combine the advantages of both empirical analysis and predictive models. Such models exploit the strengths of machine learning and deep learning methods to learn complex patterns from large amounts of data without requiring domain knowledge or the specification of feature functions. Utilizing multi-dimensional artificial intelligence, these fusion models are capable of real-time processing and analysis of information and sensor monitoring, data mining and analysis of sensors and heterogeneous information in management systems, intelligent decision analysis, and prediction across time and space dimensions. The interaction of empirical data and prediction provides a qualitative and quantitative basis for the prediction of strategic intelligence. Through fusing the forecasts of multiple models or experts, forecasting accuracy can be enhanced and the uncertainty narrowed. Natural language generation models enhance the comprehensibility and flexibility of numerical forecasting by converting prediction results into natural language, while generative models promote the generation of original data at low cost for various applications such as predicting financial distress. The development of natural language analysis models also makes it possible to process informal data, enabling the detection of potential crises as well as the identification of factors influencing future events and their predictive effects. These advances create the potential for strategic intelligence prediction and crisis early warning based on natural text and speech.
The national strategic architectures for quantum-supremacy attainment are mapped and compared in terms of capabilities, implementation timelines, and investment priorities. Supporting-evidence assessment spans funding, talent, infrastructure, and other inputs, yielding architectural gaps and leverage points of national and international significance. Analysis highlights implications for development policy and governance. The United States, European Union, China, and several others have articulated major, long-term quantum-supremacy initiatives. The commitment scale, expected utility, key stakeholders, and supporting trajectory differ significantly among actors. Quantifying funding, talent, and supporting frameworks clarifies the feasibility and fit of quantum architectures in a broader national-competitiveness context.
Explainable GenAI Fraud Defense in Hybrid Cloud Insurance Systems
Using hybrid cloud-native architecture, machine learning (ML) and AIOps technologies, this study addresses the explainability challenges of insurance fraud identification models built on proprietary telecommunications data. Internal and external enterprise data, together with historical fraud incident records, power supervised classification models for fraud detection. Explainable Artificial Intelligence (XAI) technologies, which shed light on model decision-making processes and outcomes, are essential for business acceptance, regulatory compliance, and alignment with risk-mitigation objectives. The research thus fills a critical gap in the literature, encouraging organizations to move beyond accuracy-centric goals and embrace a more comprehensive view. The findings reveal that combining data from telecommunication systems with historical fraud event data leads to more accurate models and highlights the importance of class-imbalance calibration. A realistic simulation involving a high-tech anti-fraud operation showcases the potential of a hybrid cloud-native architecture with MLOps capabilities for supporting the whole ML life cycle. With appropriate scrutiny and evaluation, these models can serve business decision-making and be deployed within AIOps procedures built on hybrid cloud architectures. Looking ahead, the investigation points to fruitful future work in two areas: enhancing the explainable capabilities of fraud models through generative AI and establishing new methodological standards for the deployment of explainable fraud detection models within hybrid cloud environments.
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