Journal of Innovative Academic Research (JIAR) | International Peer-Reviewed Open Access Journal
About Us Welcome to JIAR Journal JIAR Journal is an international, peer-reviewed, open-access scholarly journal committed to publishing high-quality, original research across diverse academic disciplines. Our mission is to provide a trusted platform where researchers, academicians, professionals, and students can share innovative ideas, scientific discoveries, and practical solutions that contribute to global knowledge and sustainable development. The journal welcomes original research articles, review papers, case studies, technical notes, short communications, and survey papers from authors worldwide. Through a rigorous and transparent peer-review process, JIAR Journal ensures that every published manuscript meets high standards of academic quality, originality, and research integrity. Our Mission Our mission is to promote excellence in research by facilitating the rapid dissemination of reliable, ethical, and impactful scholarly work. We strive to foster collaboration among researchers from different disciplines and encourage innovative research that addresses contemporary global challenges. Our Vision Our vision is to become a globally recognized academic journal known for publishing high-quality research, supporting scientific innovation, and advancing interdisciplinary knowledge across various fields of study. Scope of the Journal JIAR Journal publishes research in a wide range of disciplines, including but not limited to: Engineering and Technology Artificial Intelligence and Machine Learning Computer Science and Information Technology Data Science and Big Data Analytics Medical and Health Sciences Environmental Science Business, Management, and Economics Education and Social Sciences Agriculture and Life Sciences Mathematics and Statistics Physical and Applied Sciences Emerging Interdisciplinary Research Why Publish with JIAR Journal? International peer-reviewed journal Open-access publishing for maximum global visibility Transparent and timely editorial process Rigorous peer-review by qualified experts Ethical publishing practices DOI support (where applicable) Global indexing readiness Worldwide readership and author support Our Commitment JIAR Journal is dedicated to maintaining the highest standards of scholarly publishing. We follow internationally accepted publication ethics and encourage originality, transparency, and responsible research practices. Our editorial team works closely with authors and reviewers to ensure that every publication contributes meaningfully to the advancement of knowledge and academic excellence. At JIAR Journal, we believe that quality research has the power to inspire innovation, solve real-world challenges, and create a lasting impact on society. We remain committed to providing researchers with a credible, accessible, and respected platform for sharing their work with the global academic community.
Cloud-Native ML Pipelines for AI-Powered Payment Risk Scoring
The growing volumes of payment transactions have placed significant burden on payment service providers or financial institutions to evaluate the risk profiles of customers swiftly. Various risk-related metrics of customers can be obtained from the data related to their payment transactions, and the recent development of machine learning-based modeling approaches allows these metrics to be modeled as a risk score. However, the shoe has on the other foot as providing such services has become a competitive game between players leading to the demand for near-native response time in serving customer requests. The services, therefore, require a cloud-native big data architecture where the data generation, ingestion, processing and modeling operations can be carried out at huge scales and low response and lead times while integrating the required principles of data governance and privacy. Cloud-native IoT systems collect vast volumes of data daily that forms the data lake for processing. Data processing includes both batch and event-driven processing. Batch processing is used for generating risk-related metrics where as event-driven processing is used for carrying out risk scoring. The ML models behind risk metrics are provided as services and lifelong learning is done by using CI/CD pipelines. Firewall and observability adopt a zero-trust policy to promote not just security but also privacy and governance compliance. Robust architecture has enormous readability and scalability features for tackling such diverse services. The entire architecture is illustrated with an example in the domain of payment service providers using a risk scoring system proposed in the previous work.
Copyright (c) 2025 Journal of Innovative Academic Research (JIAR) | International Peer-Reviewed Open Access Journal
Rule-Based Automation for Audit Sampling
Automation of Audit Sampling Using Rule-Based Decision Systems: an objective, scholarly analysis of automated sampling approaches, evidence-based assessment, and formal structure. Automation and computerization in auditing are ubiquitous and offer unparalleled assistance to auditors in improving efficiency, effectiveness, and overall cost. Audit sampling is an effective means to make audit decisions based on part of the evidence rather than the whole. Rule-based decision systems are popular in many business and accounting areas, but not widely deployed for audit sampling yet. Audit sampling can be automated by creating sampling rules based on audit data, professional guidelines, and/or judgment. Such rules specify sampling conditions and thresholds, the population elements that trigger the rule-set, the sampling specification produced, and the sample sizes required and whether the auditor should consider additional information of other related decisions. The approach also supports a human-in-the-loop function, providing the auditor with automation assistance but allowing judgment to deviate from the rules. The extent of automation can vary to meet auditors’ needs and is not limited only to the mention mode of rule-based systems. Effectiveness and efficiency can be evaluated in terms of accuracy, coverage, false positives, false negatives, processing time, and resource utilization.
Copyright (c) 2024 Journal of Innovative Academic Research (JIAR) | International Peer-Reviewed Open Access Journal
Analytics-Based Supply Chain Optimization
Demand for supply chain optimization through industrial data analytics has surged, fueled by lessons learned during the COVID-19 crisis. A systematic review of methods applied in practice in 2021 summarizes how data science and operations research techniques are being adopted to tackle key issues. The review follows the ground-up flow of data in a typical supply chain. Publications describe predictive maintenance; end-to-end supply chain visibility; and inventory optimization. The data sources and processes for these analytics are also considered, echoing the importance of data quality and integration. Organizations are investing in technologies and competencies, yet progress remains slow. Acknowledgment of data quality dimensions is essential to achieve reliable models, yet limited. Moreover, a lack of interoperability hampers integration across factory-level operational systems and, consequently, impact on global visibility. These observations highlight the gap between the theory and practice of supply chain optimization. The study makes a threefold contribution to operations research and data science. It analyzes how pioneering companies are leveraging data science for supply chain optimization, synthesizing methodological applications within the identified analytics. On the data side, it discusses the sources that feed manufacturing and logistics operations, emphasizing critical dimensions of data quality and the requirements for integration across distinct analytical domains. Finally, it translates this body of knowledge into practical insights for managers.
Copyright (c) 2026 Journal of Innovative Academic Research (JIAR) | International Peer-Reviewed Open Access Journal
Multi-Agent Big Data Framework for Wholesale Food Logistics
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.
Networked Intelligence for Mission-Critical Asset Maintenance
A Distributed Cognitive Maintenance Framework enables network-operated devices to create knowledge through data-informed reasoning and local actions. For mission-critical environments, Machine-to-Machine communication speed, reliability, availability, and consistency must be secured and continuously monitored. External interfaces and device behaviour must comply with security considerations and avoid congesting network bottlenecks during abnormal operation. Data traffic volume has to be proportional to the information gained. Moreover, independent device adaptation must not cause functional degradation of peer devices nor reduce overall network preparedness to handle deviations. Existing devices operating at the edge of a Fog/Cloud environment are capable of supporting data collection and inference. They can reason locally, adapt operation to local context, and create knowledge that may benefit others. Reasoning capability, decision process transparency, and redundancy through cooperation may enable the application of cognitive maintenance, where local actions, knowledge-discovery processes, and information-sharing are applied to ensure availability. Cognitive maintenance should be considered an alternative, complementary, or add-on to preventive maintenance, condition-based maintenance, and predictive maintenance approaches.
Cross-Sector Health Intelligence Framework
Seamless delivery of health services at scale requires integrated clinical and payer data ecosystems. Interfacing these ecosystems enhances clinical-payer collaboration during service delivery while enabling predictive analysis of care paths and care-coordination workflows. During service delivery, care coordinators monitor high-risk patients using algorithmic alerts based on clinical and payer data, facilitating timely reassignment of patients to case managers. Resilience analysis assesses service-delivery during periods of demand shock. The clinical-payer intelligence fabric concept is explored for a defined clinical-payer ecosystem in Ontario. Future refinements will be guided by use-case implementation and empirical assessment. Health service delivery in a mature health ecosystem like Ontario’s is often perceived as a ‘closed-loop’ process, seamlessly transcending stakeholder boundaries and supported by partnership and trust. Yet, the interplay of clinical-payer collaboration throughout individual episodes of health service delivery and of the broader environment on health-service demand and delivery remains less well understood. Disaster, public health, and demand-supply imbalance scenarios reveal service delivery to be akin to an open-loop societal system operating at operational-risk thresholds. These tensions manifest in health-system-resilience metrics—the degree to which the ability to deliver effective services is maintained during extreme events.
Peer-Verified Compliance for Elastic Service-Oriented Systems
Enterprise service ecosystems often lack economic scaling due to the limited federated governance intelligence of stakeholders. They need a governance ecosystem framework with comprehensive compliance concepts for horizontal and vertical scalability of service enterprise ecosystems. A compliance governance ecosystem consists of partners, business owners, policies and control point decision functions, intelligence and processes. Partner policies combine local and community partner context for federated compliance preventing collateral information of services, processes or production, and incorporating service or process reuse. Partner owners peer-review service and process quality testing, although without strong business models these are rarely used for third-party service or process. Business owner policies reuse existing Federated Business Process Execution Language definitions. Horizontal ecosystem scaling creates communities of interest to ensure local business policies are supported; vertical scaling creates more complex feedback points, enabling compliance on non-business enterprise services. The Federated Compliance Intelligence Theory introduces new definitions, a layered compliance governance ecosystem model and metrics to quantify scalability of the governance context compared with ecosystem-size scaling methods of computation, memory and bandwidth. SECURE understands owners, business policies, control points and their information context as layers of a compliance governance ecosystem, externalized from partner enterprise-internal compliance decisions into enterprise service ecosystems.C
Adaptive Service Sync for Distributed Enterprise Systems
The ever-growing demand for a rapid introduction of new applications and services, coupled with the wide adoption of microservices architecture and its supporting cloud ecosystem — including public IaaS, PaaS, and serverless — has led enterprise infrastructures to become strongly distributed and heterogeneous in nature. Despite these evolutions, enterprise environments still need to provide consistent-yet-adaptive service interactions and execution modes that transparently support the reliability, performance, data, and security requirements of the applications that are being executed. This paper argues for and sketches the architecture and protocols of adaptive service synchronization across distributed enterprise infrastructure platforms that efficiently tackle the service interaction and execution-related trustworthiness aspects of heterogeneous enterprise application deployments. Enterprise microservices infrastructures have emerged as one of the major enablers for rapidly creating and deploying enterprise applications. The need for a decentralized, modular application structure, along with the recognized advantages of separating and offloading the execution of auxiliary concerns from the application logic, led to the adoption of architectures based on Business Process (BP) models and Service-Oriented Architectures (SOA). More recently, the introduction of the Service Mesh operational abstraction for BPs has further enhanced the distributed and heterogeneous nature of enterprise deployments. However, the continuous demand for adapting the quality of the provided services in terms of performance, data, and security strongly relies on the capabilities of the business BPs and external services to provide consistent coordination and safeguard time and state consistency.
Adaptive Knowledge Fusion for Care Navigation
The ability to monitor patients within their context and provide care-navigation guidance is a fundamental component of intelligent-care systems. Such context-aware care-navigation systems must be able to fuse large quantities of medical knowledge from heterogeneous sources. Knowledge fusion enables health-care services and applications to interrogate and search through data from a variety of sources tailored to a specific context, so that the sensed context as well as inferred and anticipated information can be mapped to the data resources required. Ontologies and terminologies of a healthcare domain constitute the knowledge layer and make it possible to understand, locate, and integrate raw data sources as well as reasoning services, either through lossless or lossy processes, and at different abstraction levels. However, the abundance of these resources poses challenges that to date have remained under-explored. Two main subsets of knowledge-fusion processes can be distinguished: the collection and integration of the various resources based on the intended goals and context; and the parsing and appropriate fostering and filtering of the information-flow pathways that convey the data, knowledge, and services. Strategies, methodologies, and requirements for context-aware care navigation are proposed. Supporting patients and the elderly within their general environment care through effective navigation is among the oldest goals of health-care systems. More recently, the trend towards care in non-institutional contexts has grown, fostered in part by the drive to contain costs. However, the idea of placing a patient within an operative context that can sense its changes and provide appropriate guidance has only recently emerged. The trend towards the integration of various pieces of information into a unified framework, rather than into separate, ad hoc, semi-expert systems, is also relatively recent; yet, the two concepts correspond well in terms of implementation.
Dynamic Clinical Networks for Smart Care Optimization
Objective: Self-Evolving Clinical Interaction Networks (SE-CIN) for optimization and risk-aware analysis. Gaps: Prevention seldom prioritized in real-time care. A dynamic architecture is needed. Hypothesis: SD-CIN self-evolve during learning. Contribution: General principles of self-evolution and SD-CIN Open-source implementation based on real data. Prior Work: Interaction networks support care pathway optimization but change too seldom for episodic care. Human behaviour is mainly driven by past experiences and affects future decisions. Dynamic Bayesian networks are a statistical tool to incorporate dynamic time-dependence in graphical models and can thus also reflect the aforementioned learning mechanism Natale et al. (2014). Timeliness, uncertainty, and a-priori reliability are taken into account in identification, together with the dependence on recent data labels (diseases or outcomes of treated patients) to adapt the SD-CIN in the shortest possible time. When properly tuned, DM-VaLIDE-MI permits separating the noise and focusing on the dynamically changing part of the problem. Timeliness improves reliability in care pathway optimization by reducing noise.
AI-Driven Secure Transaction Governance in Cloud Finance
Financial governance may benefit from generative AI as it promotes augmented governance by design. The resulting capability is operationalized as a blueprint for real-time secure transaction processing in cloud ecosystems. Through the design and integration of the various system layers, desired characteristics of transparency, explainability, auditability, and trust become built-in features that support and enhance the risk management, control, and decision-making processes of stakeholders across the value network by providing and assuring consistent information, even in the presence of uncertainty. Generative AI is applied to define the decision-making process, specifying how the Generative AI Governance Decision Proposal defines financial policy and compliance. Consequently, the related sense—analyze—respond model of augmented governance identifies the roles, responsibilities, decision rights, escalation paths, and governance rituals that provide the operational framework for governance. Data ingestion pipelines enrich the provenance of information to support enhanced risk–control–accountability. Transparency is specified from a governance perspective, defining policy alignment for trust in the AI–enabled financial decision outcomes.
Cloud-Native DevOps for GenAI-Powered Insurance Risk & Fraud Intelligence
Cloud-native DevOps for AI pipelines in insurance risk intelligence and fraud detection promotes replicability and composability across use cases. Support for scalable workloads addresses high costs, disk and memory limits, and specific cost and time-saving demands. Cloud-native principles—composability, elasticity, portability, observability, security, formal compliance, and data governance—are applied to underpin a generative AI-oriented insurance architecture. The DevOps component tailors continuous integration and continuous delivery (CI/CD) practices to be responsible, reproducible, reliable, and safe. Infrastructure depends on containerization and orchestration technologies to fully contemplate reproducibility, observability, isolation, and multi-cloud portability. AI workload provisioning adheres to a service model. For safe operation, specialized AI CI/CD pipelines provide model versioning and rollback, data versioning and validation, evaluation, performance metrics and safety gates for canary releases, and go/no-go decisions based on business risk appetite. Their design highlights domain-specific operational risk controls. Different generative AI applications in insurance cover a wide spectrum of functionalities, underlining the fundamental basis for the creation of these models and the capability of these applications to adapt to different environments. Nevertheless, models must be handled with care in order to mitigate potential ethical, social, legal, regulatory, and business-related issues. Risk management and operational risk must be part of the implementation which, when properly completed, can provide a safe way to integrate these types of applications. Several risk intelligence use cases point to specific implementations demonstrated during the CapGemini FBDX programme and depict high-priority paths in the integration of such approaches, illustrating data origin and target indicators.
Scalable AI Frameworks for Real-Time Distributed Monitoring and Decision-Making in Smart Automation
Scalable AI-based systems are outlined that support distributed, real-time monitoring and decision-making in the context of smart automation systems. The architectural principles presented are driven by the following requirements: Distributed sensing and data ingestion; AI methods that support real-time monitoring of large-scale sensor networks; Scalability and performance enhancement; Support for privacy; Governance and data management regulation; Federated capabilities and privacy support preserving data confidentiality. Within this framework, federated adaptive neurons are proposed. These AI nano-scopes provide local information about distributed streams, support anomaly detection and declaration in large sensor networks such as water networks, and can operate in a privacy-preserving environment. Additionally, horizontal and vertical scalability with distributed or high-performance computing is exposed. The proposed principles are supported by experimental cases that illustrate a distributed real-time monitoring capability. Monitoring of the water quality and surveillance of an aquatic environment is investigated as an application area. A Smart City use case within a European initiative is also proposed. Finally, recent advances regarding governance, security, and privacy of data, especially on personal nature, are examined. These aspects are worthy of special consideration, since data governance regulation is evolving rapidly. The analytics to monitor data that may cross data sovereignty boundaries, such as those originating from users, need to respect end-user privacy preferences.
Trusted Data Foundations for Insurance & Investment AI
To address the institutional reliance on a combination of traditional data sources and poor quality alternative data, two distinct—but related—problems linked to the Governance of Big Data are analysed. In a Big Data and AI-driven world, the modelling paradigm changes from a linear perspective to a non-linear one, leading to a shift in focus: the analysis, selection and creation of model features becomes the main modelling task and modelling is performed in a segmented way. Data quality and data processing time gain special importance. Moreover, Data Governance impacts any enterprise involved in developing decision-making tools via an AI/ML Data Modeling Paradigm. Deficiencies, inadequacies, ethical concerns and governance checks of data required for the development of risk, pricing and portfolio management models for Investment Solutions, as well as for the data driving the underwriting decision process and risk scoring for Insurance Solutions, are analysed. Finally, the underlying Technical Architecture is proposed. Specific checks, balances and design controls would help Institutions to develop Big Data-based Decision-making Tools with more robust results and safer performance.
Cloud-Native DevOps for Agentic AI in Automated Finance
Conventional automated trading and risk management employ deterministic models within a well-defined decision support paradigm. Emerging DevOps-driven cloud-native architectures, however, enable scalable, resilient, operable, observant, and automated systems where models are continuously updated in response to changing real-world conditions. Recent research suggests extending agent-based reinforcement learning beyond a decision support role toward direct decision-making responsibilities, underpinned by proper control and governance principles. The combination of such agentic models within automated financial systems presents novel challenges, while fulfilling requirements defined by supervisory authorities. A suitably architected cloud-native environment thus has the potential to not only expedite the delivery of financial AI models, but also to support their increasingly frequent deployment and operation with minimum effort and oversight. The specific considerations, from foundational concepts through to development and operational constraints, then govern the underlying implementation. The ultimate goal is a DevOps-driven cloud-native implementation framework that guarantees the reliable delivery of agentic AI models into automated trading and risk management systems, quickly, frequently, and with minimal overhead. In this highly regulated domain, controls must therefore be embedded within the supporting infrastructure and processes rather than bolted on afterwards. The key metrics for measuring success then become the speed, frequency, and simplicity with which models are delivered, continuously improved, and retired, all while remaining compliant with a multitude of ever-present security, risk, governance, and operational requirements. By explicitly specifying these considerations, the intention is to present a coherent synthesis of the architectural rationale and a repeatable implementation approach.
Architecture and Performance Tuning for Healthcare Data Exchange
The use of Health Level 7's Fast Healthcare Interoperability Resources (FHIR) for facilitating real-time data exchange among healthcare systems in life-critical scenarios enables patients to receive timely and appropriate treatment whenever and wherever they need it. FHIR provides models, rules, and interfaces for data exchange between computerized applications operating in the healthcare domain. Being a web-based standard, it can be easily deployed using an event-based architecture. Furthermore, it allows for online communications and moves away from standard file batch processes. The eventing mechanism facilitates the push of data from an event publisher to interested subscribers and notifies them of updates for resources of interest. The increasing diversity and complexity of healthcare information systems, along with the demand for timely integration of data across these systems, point toward the need for a real-time event-based integration approach using FHIR REST services. Open-source frameworks have been developed for achieving the required integration. However, architectural decisions in designing such frameworks have a significant impact on their ability to support data exchange with low latency. The selection of an appropriate integration architecture is also crucial for meeting requirements such as interaction with external enterprises, protocol translation, security, and compliance with cloud deployment models. Therefore, several architecture patterns commonly considered and deployed for event-based integration using FHIR are discussed. Each architecture pattern is detailed, along with its strengths and weaknesses for supporting real-time data exchange.
Copyright (c) 2023 Journal of Innovative Academic Research (JIAR) | International Peer-Reviewed Open Access Journal
Data-Driven Clinical Workflow Optimization
Clinical motivation, objectives, data-driven approach, methods, findings, and implications are synthesized. Clinical workflows govern patient management in specialty practices, creating a need for technique deployment to improve operational outcomes and resource utilization. Data-Driven Process Modeling uses systematic data analysis to provide rigorous evidence and formal structure to clinical decision-making. ‡Process mining identifies the real recurrence of patient journeys; machine learning models future event occurrences to replicate desirable flows; simulation of mapped processes quantifies throughput, waits, and cycle times; and hybrid methods combine these techniques. Together, they identify bottlenecks and variations affecting performance. An Emergency Department throughput case shows how analyzed paths reveal long wait times at triage and radiology, together with extended time between physician disposition and patient departure. Measurable improvements result from targeted interventions. In a second application, development of Machine Learning algorithms for segments of an inpatient discharge planning pathway highlights excessive attorney handoffs, leading to simplified processes and faster discharge times. Analysis of quantitative results indicates throughput times, waits, and cycle times are shortened, variance reduced, and changes statistically significant. ‡Qualitative evidence complements numbers by revealing practitioner satisfaction and user acceptance.
A Hybrid Framework for Retail and Paint Manufacturing
Disruptions in supply chains cause severe financial losses, necessitating exploration of Agentic AI solutions for Supply Chain Resilience Management. Agentic AI differs from traditional automation, offering innate problem-solving, adaptive-learning, and collaborative capabilities. Enabling Agentic capabilities in supply chain systems can enhance Resilience Strength by supporting accurate decision-making and expanding the range of explored solutions. Concerns regarding safety, accountability, and over-trust in Agentic systems need to be addressed. A Hybrid Agentic AI-Driven Resilience Engineering Framework comprises a central technology area for Resilience Strength and several application areas for demand and supply networks. Scenarios for Retail and Paint Manufacturing demonstrate expected contributions in terms of inventory cost, stockout level, and service level performance indicators. Supply chain disruptions incur significant financial losses. Agentic Artificial Intelligence—AI with intrinsic problem-solving, adaptive-learning, and collaborative abilities—has been highlighted as a possible contributor to Supply Chain Resilience Strength. Concerns over Agentic features such as safety, accountability, and over-trust require exploration. Enabling Agentic capabilities in supply chain systems can improve Resilience Strength by facilitating precise decision-making while broadening the spectrum of potential solutions. Agentic AI departs from conventional automation by providing appropriate support for semi-structured decision environments. Supply Chain Resilience Management based on Agentic AI can leverage these features to preemptively mitigate novice-level disruption risk, fortifying the system’s ability to absorb, respond, and recover from a wider array of causes and types of disruption.
Predictive AI for Real-Time Autonomous Vehicle Navigation under Uncertainty
Following a probabilistically-driven design philosophy, the proposed framework aims to address a pivotal aspect of real-time navigation for autonomous vehicles while constantly handling uncertainty—making predictive decisions in an uncertain environment. To this end, given a mapping of the environment augmented with statistical information for all key perturbations impacting performance during online navigation, the questions being tackled are: at each instant during online navigation (substantially constrained by latency requirements), what would be the preferred manoeuvre leading to the most favourable consequence (in terms of an appropriately defined cost function) within a defined disrupted neighbourhood of the current state? Would the definable manoeuvre, coupled with a meaningful understand of the environment at large within an horizon, guarantee that a reliable trajectory through the horizon could be sustained while dynamically adapting to key unexpected changes? To emphasise the persistent uncertainty within real-time navigation, performance is considered within a continuously dynamically evolving local environmental neighbourhood. The objective is to remain in a safe zone—away from obstacles, poor friction and slim clearance port holes—adapting the predictive decision at the end of a navigation latency to lead reliably through the next required safe region within the horizon while still reacting towards key determined perturbations. In probabilistic terms, this framework thus focuses on a novel predictive-decision scheme capable of overcoming: a unidimensional predictive approach under noise; noise constraints within a partial-observable and dynamic environment. A novel predictive-decision system bridging a low-cost inference engine and a key sensor redundancy has exhibited the capability to reliably adapt to a few sudden—and thus not directly observable—perturbational shifts.
Agentic AI for Self-Adaptive Semiconductor Architectures in Intelligent Wireless Networks
Intelligent wireless network systems must adapt in real time to channel conditions, mobile users, and traffic demands while complying with strict performance specifications. The presented study describes a foundation for agentic AI-driven adaptive semiconductor architectures, taking into account both technology and system-level considerations. With respect to technology, it focuses on recent semiconductor architectural trends inspired by early developments in AI computing. In particular, it examines reconfigurable and programmable hardware, neuromorphic processors, ternary and binary computing, in-memory and near-memory computing, and 3D integration. From a system perspective, it identifies mechanisms supporting real-time adaptation: spectrum and mobile resource management, context-aware modulation and coding schemes, and end-to-end control loops. In addition, it discusses AI-driven development toolchains, addressing tool automation, data privacy, and security issues. Finally, it proposes metrics, benchmarks, and evaluation protocols enabling a holistic assessment of adaptive semiconductor architectures for intelligent wireless networks. Attention to both technology and systems can steer forthcoming technology development towards the ultimate intelligent wireless systems and networks targeted by 6G and beyond.
Trade to Trust Real-Time AI Compliance for Derivatives
The growing demand for low-latency computing in derivatives markets challenges the traditional approach of monolithic applications implemented in dedicated data center clusters. Cloud-native architectures offer a promising solution due to the flexibility, scalability, and resilience of the cloud. Nevertheless, for latency-sensitive workloads, latency is not only a requirement; cloud-native design principles can lead to improvements in the computing model itself. These include breaking down monolithic applications into microservices deployed in a service mesh, taking advantage of on-demand and pay-per-use resources to optimize costs for off-peak traffic, and improving computing efficiency through loading-balancing across cloud availability zones. Furthermore, the demonstrably increasing risk of operational resilience in the cloud at a suitably architected cloud level requires organisations and business partners to reconsider their outsourcing risk appetite. Even where a loss occurs, insurance can cover events, as long as the incident doesn't lead to the organisation ceasing to trade for an extended period. For many services, intelligent brokerage can ensure continued service. Examined in more depth, these principles may also strengthen the fundamental efficiency and performance of the models beyond a simple scaling gain.
Governance and Compliance in Cross-Border ML Systems
Autonomous machine learning—defined as the use of a machine learning system's response (the result of an action taken by it) to enable further decision-making without human intervention—has profoundly impacted global finance. Such an impact is manifested in flows of funds for transactional business in products and services, financial exchanges, and financial assets across borders. A clearer separation appears between the actual originator of the transaction and the funds-handling entity in other jurisdictions that provide transaction settlement or routing. This also creates risk and compliance challenges associated with these cross-border transactions in that the funds-handling entities must deploy systems that cover Know Your Customer, Anti-Money Laundering, sanctions, and other applicable laws. Turning cross-border data-sharing needs into reality remains a challenge. For certain data-sharing needs that emerge in cross-border financial interactions involving autonomous machine learning, the risk-based allocation of roles has led to initiatives to explore building data-sharing agreements with specific jurisdictions. The principle is that a transaction flows through jurisdictions with lower-risk data-regulatory considerations, thus enabling real-time flow of information without having the data remain for long periods in the jurisdiction with stringent data-regulatory requirements.
Generative AI DevOps in Real-Time Finance
The emergence of Generative AI (GenAI) unlocks new capabilities in software development and operations. A related trend calls for the adoption of DevOps principles and practices. Furthermore, organizations across industries seek to process data in real time. In financial services, that imperative applies to transaction pipelines, where volumes peak at Black Swan events, yet the average workload is relatively modest. Ideas from GenAI-enabled DevOps can accelerate the design, implementation, and monitoring of such pipelines, but tensions remain. The landscape of existing work is therefore reviewed, gaps identified, Generalized Enterprise Architecture Theory summoned, and research questions posed. Addressing them will yield candidate solutions that reconcile GenAI-enabled DevOps principles with the requirements of real-time transaction processing. A foundation for that exploration is laid by: specifying the functional and nonfunctional requirements for such pipelines; providing an overview of GenAI-driven DevOps concepts, architectures, and operational paradigms amenable to financial Pipelines; and articulating suitable patterns, together with the associated challenges and trade-offs. Real-time transaction processing pipelines in financial services must support Black Swan event volumes while meeting operational requirements at lower loads. Ideas from GenAI-enabled DevOps can accelerate the design, implementation, and monitoring of such pipelines, but tensions remain. Existing work is reviewed, gaps identified, Generalized Enterprise Architecture Theory summoned, and research questions posed. Addressing them will yield candidate solutions reconciling GenAI-enabled DevOps principles with the requirements of real-time financial transaction processing. A foundation for that exploration is laid by specifying the functional and nonfunctional requirements for such pipelines; providing an overview of GenAI-driven DevOps concepts, architectures, and operational paradigms amenable to financial pipelines; and articulating suitable patterns, together with the associated challenges and trade-offs.
Cloud-Native Agentic AI for Insurance Fraud Detection
The study investigates DevOps-driven data engineering for agentic AI in insurance fraud detection with a focus on cloud-native automations. Data are centrally secured, accessible, governed, of reproducible quality, properly profiled, and efficiently prepared. Design, development, and deployment encompass extensive safeguards for risk-sensitive decision-making. A superset of continuous integration/continuous deployment tailored to machine learning as well as data governance underlie the approach, demystifying the technology stack and enabling success at operational scale. The investigation is motivated by regulatory, economic, and ethical pressures on the industry, demand for predictive solutions, and pain points of legacy monitoring systems. Automation of machine-learning model development and deployment mitigates issues of scalability, reproducibility, and security while incorporating risk assessment. Organizations worldwide are increasingly turning to artificial intelligence in a bid to detect fraudulent activity in all its forms. Such systems, however, generally stem from one-off proof-of-concept initiatives, operate in isolation, and lack sufficient controls for operational deployment in support of everyday decision-making. Nevertheless, agentic artificial intelligence—actant solutions capable of executing decisions without direct human intervention—promises a transformative competitive advantage for business leaders. Integrating governance and control ensures that these automated systems operate within an acceptable risk tolerance, align with organizational values, and minimize unintended consequences. Insurance fraud detection solutions typically fall short of these requirements.
AI-Driven Big Data Systems for Climate Change Impact Assessment
AI-Powered Big Data Systems for Intelligent Climate Change Impact Assessment presents an objective, evidence-based account with formal structure and clear, discipline-appropriate critique. Growing climate knowledge enables analysis of weather events and future scenarios using AI and Big Data systems. However, such analyses must increasingly probe extreme impacts. While state-of-the-art statistical approaches are effective for specific detection and attribution, they cannot fully capture biophysical mechanisms or future scenarios. Probabilistic projections need a causal perspective. An emergent intelligent climate change impact assessment and projection framework integrates detection, attribution, projection, and scenario modeling with these additional aspects. Multisource AI-assisted datasets support an international ensemble of studies on subcontinent-level impacts. A regional vulnerability mapping exercise interprets sensitivity to climate extremes, assesses uncertainty, and identifies regions at risk. Future scenarios measuring climate-disrupted carbon and ecosystem service balances are generated from a continental land model. Nevertheless, local risk assessments rely on simple calibrated approaches, thus remaining vulnerable to missing mechanisms in the statistical relationships.
Smart Demand Sensing Cloud AI for Retail Inventory Optimization
Demand sensing is the practice of using real-time or near-real-time information to understand customer demand variations and to take action commensurate with the temporal nature of those changes. The objective is to be responsive to real-time demand signals and address the variations within the overall demand trend. Demand sensing can bring significant benefits to the supply chain and support key processes such as inventory management and planning. In the context of retail, it is particularly useful for managing perishables and fashion goods. Although demand sensing and forecasting are distinct, they are complementary. Forecasting is typically focused on the longer-term view, while demand sensing emphasizes real-time signals. Demand sensing is limited by the availability of real-time signals, and their freshness, quality and relevance also need to be considered. A fresh real-time signal is imperative to make reliable, short-term adjustments.However, a signal with a high volume and frequency can result in noise and unnecessary response. High-velocity data arrives at high speed into Data Lakes. Time-sensitive decisions must be based on these transient signals.
AI-Driven Leakage Detection & Water Network Analytics
Water utilities are under growing pressure to improve operational performance and service levels while conserving energy and resources. Predictive analytics—including time series forecasting, anomaly detection, leakage detection, and leakage prediction—can inform data-driven decision-making for smart distribution systems. The overall research is designed to explore and implement predictive analytics for smart water distribution systems and leakage detection, with key contributions including a temporal analysis framework to extract time-dependent patterns from management-relevant data streams, and techniques for the detection of anomalies in flushing patterns, as well as the detection and prediction of leaks. Water losses from ageing infrastructure, corporate theft, infiltration, and leakage combine to impose economic, social, and environmental costs. Although regulation is often a key driver for leakage detection and mitigation, proactive management is typically more cost-effective than responding to external pressure. Indeed, decision support tools can facilitate the identification of optimal solutions with respect to detection priority, repair timing, and budget allocation. Predictive analytics—including time series forecasting, anomaly detection, leakage detection, and leakage prediction—can support data-driven decision-making for smart distribution systems by exploiting existing data streams that are often recorded at high frequency.
The study investigates the use of autonomous Artificial Intelligence (AI) systems for enterprise knowledge automation and digital workflow optimization. Enterprise knowledge automation transforms tacit and experiential knowledge into digital representations while digital workflow optimization automates repetitive and decision-based tasks. Such systems integrate and orchestrate planning, decision making, and execution capabilities. Various architectural patterns and technologies facilitate autonomous knowledge acquisition, representation, reasoning, decision making, integration, ecosystem compatibility, workflow orchestration, task automation, and event handling. The research aims to design autonomous AI systems that align with enterprise objectives, enhance performance, and enable transparent evaluation. The intent is to demonstrate how enterprise systems with knowledge automation and digital workflow optimization capabilities can improve operations across various functions. The main outcomes comprise a knowledge management system with support for content recommendation, an AI-enhanced customer support chatbot, and a business process managed integration of different functional units. Enhanced learning and adaptive capabilities are expected in addition to process efficiency. Knowledge acquisition, representation, and organization, as well as conversational interface design, personalization, contextual coherence, and consistency, are considered crucial. Additional functionality covers multi-expert assessment, event-based business process execution, and the ability to cope with alternative incident resolution paths.
Self-Governing AI Agents in Digital Banking
Digital banking is fundamentally reliant on the Digital Automation of Banking Operations Workflows (DABOW). DABOW are governed by internal banking risk management policies, but they factor neither genetic tendencies, impersonation attempts nor socio-economic conditions through the resources traditionally provided by external actors. Expansion into end-to-end Digital Banking Workflow Automation (DBWA) allows the introduction of autonomous AI agents controlling DABOW and monitors by weaving external AI resources and Reasoning Services into the Banking Automated Notification Infrastructure for Handling External Requests (BANIHHER) framework, ensuring natural language processing and robotic process automation ease. Conformity to global practices of Digital Banking extends the Banking Automated Notification Infrastructure for Handling External Requests – Transaction Processing (BANIHHER-TP) design to encompass Customer Onboarding and Identity Verification (CO-IV) for secure user profile creation, Transaction Processing and Fraud Detection (TP-FD) for trusted transaction processing and Fraud Extraction (FE), all enabling service level performance. Incorporating cyber intelligence services and infrastructure augments DBWA with Cognitive Data Management and Breach Prevention Monitoring to enhance response efficiency. The depth of technology reliance and tautology practiced by modern banking strengthens its appeal as a target for manipulation through model integration. Deployments as Division Chief Officers of Cyber Manipulation Networks, targeting the shaping of fake news using psy-ops, cooperate and coordinate the First Blockchain Design Acceptance, System Analysis Synchronization Simulation, and Predictive Analysis of Blockchain Networking Synchronization and Displacement Detection in Lab Design as visible. Pathology Closure Network status and Controls Enactment Network operational control reinforce adoption.
Big Data AI for Smart Supply Chain Risk Forecasting
Unprecedented disruptions, such as natural disasters, pandemics, geopolitical conflicts, and other unanticipated scenarios, impose significant challenges on global supply chains. Supply chain disruption prediction is a crucial area of supply chain management, enabling proactive risk management and resilient operations. AI-driven big-data platforms integrating heterogeneous data from multiple domains can help identify such unexpected disruptions. Data ecosystems consist of the various sources needed for the prediction task, along with processes and technologies for integration and data quality assurance. Data quality, privacy protection, and governance are often crucial factors, especially when dealing with personal, sensitive, or regulated information. Multiple modeling approaches—time-series analysis, anomaly detection, graph-based methods, and network analytics—can be operated in parallel and combined. Scope and prediction quality can depend on deployment scenarios, for example, in the procurement, supplier, transportation, and logistics domains. The AI-driven platforms enable the early, intelligent prediction of disruptive events. Timeliness remains a key requirement, given the speed of many supply chain processes. Predictive performance can be benchmarked using dedicated metrics, including quality and speed. Jeremy Haefner’s vision of a collaborative, co-designed platform should also be applied to validation; industry partners should provide test data from private data lakes for calibration and subsequent progress testing.