The American Journal of Analytics and Artificial Intelligence (AJAAI)
The American Journal of Analytics and Artificial Intelligence (AJAAI) is a peer-reviewed, open-access academic journal dedicated to advancing research in analytics, artificial intelligence, machine learning, and computational intelligence. The journal publishes original research articles, review papers, and case studies that address both theoretical foundations and practical applications across science, engineering, healthcare, finance, and social domains. AJAAI is committed to rigorous double-blind peer review and to making high-quality research freely accessible to scholars, practitioners, and policymakers worldwide. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, all content is available without subscription or access barriers.
Significance of Negative Matric Suction Pressures on Infiltration in the Swash Zone_Final
The swash zone's complex hydrodynamics can be influenced by infiltration and exfiltration processes, which are not yet fully understood. This study explores the role of negative matric suction pressures in swash-zone infiltration, focusing on seven naturally occurring beach media samples. Using a combination of tensiometer and in-contact filter paper methods, we examine the prevalence of matric suction pressures for beach media ranging from fine pebbles to fine sand. The results highlight the existence of matric suction pressures within the range 0-2 kPa for the examined media samples, with maximum values observed within the grain-size range 0.85-0.71 mm. Our results reveal a previously underexamined aspect of swash-zone infiltration, highlighting the potential significance of matric suction in influencing coastal processes. The findings suggest that omitting matric suction effects is shown to be an invalid assumption under certain conditions, as these pressures can exceed gravitational potential.
Copyright (c) 2025 Anton Ali, Damian Alexander (Author) https://creativecommons.org/licenses/by/4.0
Scalable Cloud Systems for Automated 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) 2025 Michael Anderson (Author) https://creativecommons.org/licenses/by/4.0
Integrated Governance Model for Industry 4.0 Data Centers
Digital transitions are revolutionising industrial processes and services offered. The new arrangements introduce new risks across the entire value chain domain from planning to supply, production and delivery of services. State-of-the-art Enterprise Risk Management (ERM) provides the necessary elements to cover risk domains associated to cyber-physical systems interconnected by the operating technology in concert with Information Technology under Industry 4.0 paradigms Control mechanisms from ERM needs new support structure provided by the Data Centre Governance principles, in particular those linked to data quality, security, privacy and stewardship, for a coherent data preparation and management able to deliver the correct and timely analytics supporting the decision making. The effort synthesises the integrated Enterprise Risk and the Data Centre Governance Framework, integrated with data quality requirements, applying the two best practice domains to the Industry 4.0 era. The underlying concept defined a layer governance structure able to cover day-to-day operate in the production/service delivery sites and the strategic management levels for the application to any organisation supporting the integrated risk management approach. The set of guiding principles is outlined consequently. Grounding elements are drawn from literature sources, the originals support description from the Centre for Internet Security, the Industrial Internet Consortium guiding principles and recommendations, five case studies of Industry 4.0 environments for Manufacturing and the integration of cloud technologies in data centres. The resulting work delivers an integrated structure capable to capture coherent data from the production/service area and to deliver the correct analytics behind the ERM risk management for any Industry 4.0 context.
Copyright (c) 2024 Anumandla Mukesh (Author) https://creativecommons.org/licenses/by/4.0
Fusing Generative and Agentic AI for Insurance Fraud Resilience
Fraud poses ongoing operational and financial challenges to the insurance industry, which rely on expert knowledge to prepare for and react against ever-evolving attacks. Beyond discrimination of potential fraud scenarios, future detection systems must therefore support the synthesis of training data for evolving patterns, surface latent signals for unseen forms, propose tests for the detection of adaptative fraud patterns and scenarios for benchmarking, and automatically adapt to changes. Recent advancements in the fields of Generative AI and Autonomous Decision making propose highly complex agents able to reason around natural language, map textual to 3D spaces, and learn new object categories from a few examples. Such models can find application to these aspects, and a study plan explore the hybrid integration of Generative and Agentic AI. The research takes the shape of a Do-Observe-Learn cycle, with the first cycle concentrating on Detect, along with Detect processes for Synthesis, Adaptation, and Quality Layer, thus applying the original focus of fraud detection to the new context, identifying the critical elements from which new decisions can be made, concluding with the resulting TDCC basis for realisation. An analysis on how evolving patterns can influence the design is also performed, targeting integration path of the hybrid design, with the generative component directly involved in detection pipeline in layer supporting adaptation. One important instance of integration focuses on the Data Quality Layer, combining external knowledge with the concept of absolutely difficult-to-detect forms for fraud detection, thus synthesizing signals that should be present in the data for the adapted detector to generalise the aspect.
Copyright (c) 2026 Ghatoth mishra (Author) https://creativecommons.org/licenses/by/4.0
Predictive Data Systems for Sustainable Farming and Hospitality
Scalable Big Data Processing and Predictive Decision Systems for Sustainable AgriTech and Smart Hospitality align with principles of growing Big Data by maximising processing throughput while minimising completion time and resource consumption. Decision systems in the background support immediate predictions, long-term forecasts, and model evaluations, providing predictive analytics for Smart Hospitality and sustainable AgriTech. Artificial intelligence models for predictive irrigation have a data-driven architecture with large-scale data from public repositories and real-time weather feeds. Smart Hospitality applies a multi-timescale approach for resource and operational cost management via demand forecasting and optimal capacity allocation. Environmental and operational impact narratives form an integrated life-cycle perspective of practice in AgriTech and Smart Hospitality. Growing volumes of data and information sourcing within hospitality operations fuel the need for predictive analytics. Predictive systems supported by environmental and mobility information are important drivers for Smart Tourism and visible within Smart Cities.The capacity and flow constraints of hospitality environments become decision system use cases for demand forecasting and resource allocation. Data-driven decision systems predict demand streams and architecture support management of hospitality processes.
Copyright (c) 2024 Shashikala Valiki (Author) https://creativecommons.org/licenses/by/4.0
A Unified Big Data Infrastructure for AI-Driven Therapeutic Personalization
AI—driven precision medicine requires access to large amounts of clinical data together with detailed clinical annotations. Clinical data platforms support this need by integrating patient records, diagnostic data, and clinical research data within a collect—once, use—many approach. Rich patient records combined with scalable analytics platforms enable the reuse of clinical data for novel analyses. Such platforms create new clinical learning loops, allowing the delivery of genomic—guided precision therapeutics to more patients. State—of—the—art clinical data platforms support dynamic, integrated clinical records, simplify cross—modality data collection, and facilitate the clinical adoption of AI technologies. The democratization of clinical data, imaging, and genomic data into open foundations allows the global research community to build foundation models that can later be adapted for clinic—specific contexts or for other clinical functions. Such foundation models offer rich transfer learning opportunities that reduce the cost of model training while increasing performance and addressing clinical bias. Emerging architectures—modelling techniques and privacy—preserving technologies address data interoperability challenges, supporting the creation of true population datasets at scale and at equal cost for all communities and demographics.
Copyright (c) 2026 Dasari Vinay (Author) https://creativecommons.org/licenses/by/4.0
Reimagining Clinical Data Architecture for Personalized Care
Copyright (c) 2024 Bhasker Katta (Author) https://creativecommons.org/licenses/by/4.0
Smart Cloud Ecosystems for Precision Healthcare Decision Systems
Copyright (c) 2023 The American Journal of Analytics and Artificial Intelligence (AJAAI) https://creativecommons.org/licenses/by/4.0
Cognitive Cloud Ecosystems for Industrial Foresight
Industrial organizations are rapidly adopting cloud technologies to reduce infrastructure management costs and increase operational flexibility. Nonetheless, existing cloud deployment uses are mainly focused on infrastructure as a service and data storage. In parallel, the demand for data-driven predictive modeling has grown and continues to evolve beyond standard business intelligence. As a result, cloud-native software development and deployment models have attracted significant attention from academia and industry. However, related research into the cloud-native paradigm remains limited and tends to offer design and development support for a single application or service without addressing the complete data ecosystem. The data processing and predictive modeling lifecycle requires a cloud-native architecture that supports large-scale predictive modeling operations and the construction of a data-oriented infrastructure. Building intelligent capabilities involves orchestrating all machine learning operations in the cloud, integrating advanced machine learning lifecycle frameworks, incorporating feature stores, and enabling real-time scoring orchestration and execution algorithms. A predictive decision-making data ecosystem focuses on the complete data processing pipeline in relation to predictive models, integrating model data processing and inference pipelines into orchestration and scheduling frameworks while supporting event-driven workflows.
Copyright (c) 2025 The American Journal of Analytics and Artificial Intelligence (AJAAI)
Real-Time AI Compliance at the Speed of Financial Fraud
Smart integration of generative AI with event-driven streaming architectures offers a promising avenue for enhancing Financial Crime Compliance. Presenting an approach for Intelligent Financial Crime Compliance (IFCC), this paper defines, delineates, and discusses the concept before illustrating its application in the context of strengthening detection of money laundering (ML) and terrorist financing (TF) activities. Generative techniques for detection, data risk/scoring, and anomaly identification provide accurate, explainable, actionable, and adaptive insights into potential compliance breaches. Evaluation frameworks tailored to the streaming regimes of compliance workflows address the urgency and operational nuances associated with real-time AML/CFT surveillance. Fed by diverse continuous data feeds and often governing timely responses to regulatory penalties, Financial Crime Compliance (FCC) processes traditionally viewed through a descriptive lens comprise a rich target for AI-enabled automation within a data-driven paradigm. Gaps in rule-based detection technologies—designed in hindsight for known patterns of illicit behavior or anomalies and lacking continual risk calibration—emphasize the need for a data-centric streaming architecture. The intensifying real-time nature of ML and counter-terrorism financing activities further motivates broad streaming detection. At the same time, the availability of such vast data resources in near real-time offers potential to improve compliance decision-making, including for risk scoring, alert generation, and near real-time data lineage.
Cross-Sector Model Deployment Intelligence
MLOps-enabled intelligent systems automate the deployment of machine learning pipelines that can exploit machine learning capabilities and pre-existing data across data platforms in multiple sectors, enabling automated decision-making with a known (soft) risk or loss function. MLOps concepts enable new forms of machine learning model lifecycle management that may leverage autoML and human-in-the-loop approaches for model development, training, and tuning; they facilitate the continuous operation of pipelines on a 24/7 basis; they address concerns such as privacy, security, model bias, transparency, explainability, interpretability, and inclusivity; and they provide mechanisms for automatic scaling, fault-tolerance, and infrastructure cost optimisation. Automated model deployment represents a major feature of MLOps. Automated deployment refers to the management of machine learning models and pipelines so that the entire lifecycle from development to deployment and operation may be governed from an MLOps perspective. When system performance is measured within a known range and against a soft loss or risk function, virtual data platforms that encompass sectors as diverse as finance, healthcare, retail, and telecommunications possess feature sets supporting such automations. Such automations focus on Machine Learning as a Service (MLaaS) deployments on a 24/7 basis, with the objective of enabling decision making within these soft constraints and scaling the deployment of autoML pipelines in tandem with demand for untrained services.
Copyright (c) 2023 The American Journal of Analytics and Artificial Intelligence (AJAAI)
Edge Intelligence for Predictive Civil Asset Management
Innovations in artificial intelligence (AI) and predictive analytics are driving the convergence of big data, sensor networks, the Internet of Things, and cyber-physical systems into environments known as smart cities. By augmenting conventional city information systems with predictive modeling capabilities that exploit the wealth of high-frequency operational data generated by sensor-equipped urban infrastructure assets, it is possible to implement proactive preventive maintenance strategies that promise to enhance infrastructure robustness, safety, performance, sustainability, and cost-effectiveness. Such strategies, termed predictive maintenance, exploit predictive models of asset health (measured via condition indicators), typically learned using supervised learning techniques, and create actionable information to optimise maintenance scheduling and better allocate limited maintenance budgets. Despite considerable interest in predictive maintenance research, most efforts have focused on specific domains (e.g., buildings, roads, bridges, tunnels) in isolation. Thus, a big-data framework architecture that provides for the integrated prediction of asset health indicators across different urban infrastructure systems and their underlying sub-systems is developed. In the proposed framework, an architecture for the ingestion and integration of diverse high-dimensional data types is presented, together with a scalable ensemble-learning-based approach for feature engineering that exploits the multi-variate time-series nature of the data generated by real-world monitoring systems. By providing a generic architecture for the predictive maintenance of infrastructure assets, the framework lays the foundations for effective predictive maintenance strategies in smart cities.
Copyright (c) 2024 The American Journal of Analytics and Artificial Intelligence (AJAAI)
Self-Evolving Data Ecosystems for Predictive Enterprise Clarity
Two significant developments have converged: the emergence of cloud-based data lakes, conducive to cost-effective data storage and processing for intelligent enterprise applications, and the application of artificial intelligence (AI) to greatly improve the various processes required to build and maintain a data lake. Intelligent enterprise applications can be categorized as descriptive analytics and dashboards; advanced analytics and predictive modeling; and real-time analytics and streaming data. These categories map to typical application areas such as customer analytics and personalization; operational intelligence and asset monitoring; and supply chain optimization and risk management. The primary issues in building a cloud-based data lake include data privacy and sovereignty; fairness, bias and explainability in AI; performance management and cost optimization.The abstract now conforms with the rest of the paper. Data governance and cataloging; security and compliance; architecture and components are addressed. Emerging AI technologies significantly enhance the intelligent enterprise applications in a cloud-based data lake, particularly data ingestion and feature engineering; automated metadata enrichment and lineage; and data quality and cleansing. Supporting both the descriptive and advanced analytics categories of intelligent enterprise applications, the Intelligent Enterprise Analytics and Reporting Framework comprises descriptive analytics, advanced analytics, real-time analytics and streaming data; integrating the domains of customer analytics and personalization; operational intelligence and asset monitoring; supply chain optimization and risk management.
Smart Agriculture Climate Adaptation via AI Big Data
Climate change is threatening the future of agriculture worldwide, and significant adaptation efforts will be needed to mitigate its consequences. Although many adaptation strategies have been proposed, a comprehensive climate-adaptation framework for smart agriculture based on AI-enabled big data are still lacking. To bridge this gap, a cloud-based Cyber-Physical Environment for Smart Agricultural Climate Adaptation was developed by integrating big-data sources needed for climate-adaptation simulation, prediction, management, and decision-making. The resulting Information, Data, Process, Software, and Model (IDPSM) ecosystem integrates supervised and unsupervised predictive-analytics models, downscaling and microclimate-assimilation Modeling, and weather-and-microclimate-scenario-planning tools, and supports AI-enabled decision-making. Several representative Climate Adaptation Case Studies were deployed in various global contexts to demonstrate the use of the IDPSM ecosystem and its associated AI methods for climate-adaptation simulations, impact assessments, and/or decision making. Lessons learned from these case studies illustrate the potential of deploying such an AI-enabled big-data ecosystem to drive data-centric climate adaptation for agriculture.
AI-Powered Fraud Detection and Risk Intelligence in Banking and Insurance
AI/ML technologies are exploited to detect, mitigate, control, and minimize risks in banking activities as they create, transmit, lend, invest, insure, and secure value. AI/ML paradigms and technologies based on microservice architecture support dynamic delivery of probabilistic native support of banking actions and transactions. Notable categories of AI/ML technologies are based on risk intelligence, fraud detection and prevention, and fraud control. Risk intelligence examines the risk area of the banking ecosystem. Risk intelligence warns participants about the risks involved in their actions or transactions, predicts a credit card operation's logical behaviour in order to set dedicated limits, and determines the best approach to set a limit for a credit score. Fraud detection identifies fraud in real time using analysis of users’ attributes and account history, monitor behaviour changes or sudden changes of an attribute, and classify whether an operation will be done by a legit user or not. Fraud prevention detects fraud before it occurs using knowledge of historical cases to find the relationship between data in order to alert when a similar case may happen and detect unusual patterns to create rules for risky operations. Fraud control deals with the management of fraudulent approaches, actions or transactions for the banking sector. Practices for fraud control address identifying tendencies in the banking sector for fraud detection—vent consumers’ intolerance for crime, minimize social disparity, increase competence to minimize price of fraud.
Copyright (c) 2026 The American Journal of Analytics and Artificial Intelligence (AJAAI)
Agentic AI for Demand, Credit, and Inventory Intelligence in Food Supply Chains
A multi-cloud big-data architecture delivers predictive services for demand forecasting, credit risk modeling, and supply chain inventory financing. Strategic partnerships provide a comprehensive range of data for a national food service domain, supported by data governance and management protocols. Predictive services facilitate collaborative demand reconciliation across heterogeneous cloud environments, deepening data interaction among competing firms, while optimizing Steering Committee members’ inventory levels and exposure to credit risk from suppliers, distributors, and retailers. Agentic Artificial Intelligence, integrating the principles of Autonomous Analytics and a multi-agency governanc­e framework, performs the groundwork for decision signals regarding all three services. While agentic artificial intelligence (AI) enables autonomous data preparation, predictive analytics, and deep learning, it does not remove humans from the loop. Instead, agentic AI facilitates deeper human-AI collaboration across the multi-cloud big-data architecture, with the a­gentic AI agents charged with examining results and providing interpretable explanations. Although solutions are focused on a national food service supply chain formed by the collaboration of food manufacturers, distributors, and both commercial and governmental businesses, the framework is readily applicable to other large supply chains requiring data preparation, predictive demand signals for collaborative reconciliation, risk assessment, and inventory financing facilitation.
Real-Time AI Risk Management for Multi-Party Derivatives Trading A Cloud Compliance Framework
The problem of real-time risk for enterprise-scale derivatives, on the sell-side business, and the orchestration of workflows across multiple counterparties, such as for FX, are well understood; indeed, gap analysis templates exist. Yet no solution is in production. Hedge proposals, whether for market-making services or for wholesale multi-country liquidity coverage fundamentals in bankruptcy planning, are self-evidently Rule 63 costs for which risk-based decision engines at enterprise scale should generate a valid counterparty profile with risk-adjusted positions, identifying the best bid/offer patch for execution and settlement. Furthermore, no architecture, systemic or architectural, meets the needs of all key stakeholders and disciplines. Leveraging the capabilities of a cloud-native foundation and embedding an AI-driven decision engine within the distinction between decision and execution engine on observed latencies supports risk-aware automation and real-time orchestration. It also strengthens the integration of risk management, regulatory compliance, and risk governance into a coherent whole across the previously examined views of risk types, risk data, the control plane, and the regulatory requirements of the system.
Real-Time Decision Intelligence Using Adaptive Deep Learning
Adaptive deep learning models are needed to support real-time operations in dynamic business environments characterized by fast state changes. Streaming data processing architectures, model adaptation mechanisms, and decision-trust capabilities form the building blocks for adaptive architectures. Adaptation relies on a spectrum of techniques, from online learning and continual learning through meta-learning to lightweight and edge-enabled neural networks. Evaluation is also challenging: approaches must be benchmarked in dynamic non-stationary settings using appropriate real-time metrics. In addition, vigilance is needed to ensure robustness, fairness, and compliance in controlled areas, such as financial services and healthcare. Real-time adaptive support has been achieved in practical scenarios involving supply chains, healthcare resource allocation, and financial-services operations.
AI-Powered Decision Support for Modern Enterprises
Enterprises generate large volumes of unstructured information in reports, presentations, and conversations. This information often resides in diverse, distributed data sources, limiting its use for direct decision-making and leading to the adoption of adhoc workflows. The delay, inaccuracy, and poor governance of such work increase risk. A variety of solutions have sought to improve the integration of knowledge work into enterprise processes. A Retrieval-Augmented Generation framework, augmented with a context-preserving knowledge-retrieval layer, provides decision-support capabilities across preset operation, logistics, planning, and compliance domains. Tools, interfaces, and monitoring dashboards allow for real-time context creation, approval, and rollback during LLM inference. Data provenance tracks root sources, promotes trust in responses, and accelerates compliance audits.
Neural-Bayesian Models for Agile Business Environments
Growing algorithmic complexity and stringent response requirements inhibit the wide-scale deployment of neural networks for business decision support. While Bayesian methods for quantifying modeling uncertainty introduce latency overheads typically incompatible with real-time operation, hybrid architectures promise the best of both worlds. Yet, existing ensembles lack the necessary expediency. Combined with the need for accurate risk assessment in dynamic environments, a neural-Bayesian model is developed and evaluated for critical support, specifically for decision-making in dynamic environments. Real-time deployment requires fast inference with low-latency data updates. Meeting these criteria within a hybrid architecture requires special consideration of component data streams and training protocols. In domain-relevant environments, temporal data sparsity enables a direct mapping from the input feature set to the neural subsystem and the early exit of superfluous data from the neural model. The real-time objective also allows for a non-standard online learning procedure involving offline model pretraining, updating during active drift only, and online drift detection with rollback for sudden change. For both systems, the decision-theoretic formulation of risk mitigates the challenge of accurate calibration without the requirement for a dedicated calibration set.
GenAI-Driven Adaptation in Next-Generation Automotive Human–Machine Interaction
The rapid improvement of interactive generative artificial intelligence systems, such as ChatGPT and DALL·E, enables ample opportunities for generative AI applications in various fields. In the context of the automotive domain, their utilization is expected to provide personalized in-vehicle experiences and services, as well as behavior-adaptive interfaces for both drivers and passengers. Recent advances in natural language processing and image synthesis open new horizons for addressing the technical challenges of enabling personalized infotainment experiences and behavior-adaptive multimodal interfaces in vehicles. Infotainment systems can benefit from their user intent modeling ability to deliver contextually relevant responses, while the rich generative models trained on large-scale visual datasets can enhance communication of driving states and intentions. Inferring drivers' emotional states from physiological signals enables the incorporation of multimodal signals into the design of intuitive interfaces that adapt to user behavior, situational context, and mental stress. Generative AI for Personalized In-Vehicle Experiences and Behavior-Adaptive Interfaces: adopt an objective, evidence-based scholarly tone; present clear, formal arguments with structured progression and well-supported claims. Research design is delineated within a dedicated sub-section. The discussion covers user intent representation and preference modeling, design principles for behavior-adaptive interfaces, and multimodal interaction in automotive environments. Data acquisition, privacy, and ethical aspects receive attention. Data acquisition, security, and consent are addressed alongside fairness, transparency, and accountability considerations. System architecture is examined in terms of among on-device versus cloud-enabled personalization, real-time inference and latency constraints, and safety-critical integration. Evaluation encompasses user-centric methodologies, objective metrics for adaptivity and usability, and simulation and field trials. Finally, deployment scenarios and applications are explored, focusing on infotainment personalization, driver state and stress monitoring, and route and context-aware adaptation.
Scholarly research is by and large an outcome of funding received by individual or collaborative efforts. The criteria of major funding agencies in a region place constraints on not only contemporary research choices but also on intellectual development over longer career trajectories. Many funders have their focus areas predetermined to address topical challenges of societal relevance, and these areas receive higher priority. Consequently, research by a larger number of scholars falls outside priority areas, that research is often beyond the spotlight and appears to move at a slow pace. The political parties in power and their support agencies taste success only when high-profile issues are attended to, albeit in the form of big-bang solutions. Since there is always a plethora of problems in the country, funding priority areas tend to be repetitive over different funding calls. The normative aspect of funding agencies in a country-and its intensity act as a guiding compass to steer the direction of research, irrespective of an individual’s intellectual aspiration. The lists drawn up by API and CARE committees place undue importance on the quantity of research publications, irrespective of the funding and nature of research. Even simple research studies that require no more than a year of work and intellectual thought are found to be published in bulk. Quality is compromised for quantity. Care is taken to ensure that these publications appear in journals that find a place in the API/CARE lists. The nature of promotion and appointee selection norms need not be explained, as these are the common expectations for attaining recognizable success in an academic career. The pattern of workload distribution also has a significant role to play in influencing research direction. An unequal share of class and administrative work prevents these individuals from investing quality time in research. Given the importance of these issues, an experiential audit was conducted to ascertain whether or to what extent funding norms, the API/CARE list, promotion criteria, and workload distribution influence research choices.
ML-Driven Compliance Auditing in Manufacturing with Digital Twin Integration
Compliance auditing aims to assess adherence to regulations or standards, which is critical for managing operational risk. Regulatory drivers, including public expectation of sustainable practices in manufacturing, create urgency to introduce integrated compliance auditing into the industry. Although innovation in compliance auditing could significantly reduce the operational risk and increase the efficiency of businesses involved, the approach has received little attention in the literature. The current study investigates how predictive models of compliance outcomes can support auditing tasks and integrate into a digital-twin framework to provide forward-looking assurance. Research questions explore how predictive models can be developed on a specific data architecture to support compliance auditing and how a digital-twin model can provide the data needed to implement these predictive models. The study applies a mixture of methodologies: prediction models capture compliance outcomes for a specific manufacturing plant, while the auditing process integrates machine-learning predictions into a digital-twin framework. Predictive models have been developed to support the specific industry context and inform compliance auditing. These models address both compliance auditing components of risk and capability. Risk-scoring models indicate violations of risk thresholds identified by audit procedures, and capability-scoring models indicate process capability for the assessed compliance aspect.
Deploying Agentic AI Across Insurance and Retirement Products
The integration of agentic AI into enterprise data products, following established principles of strategic decision-making, significantly improves agent-in-the-loop decision-making within insurance and retirement sectors. Decision-making systems underpin a broad spectrum of enterprise intelligence solutions, enabling human decision-makers to harness advanced AI technology, data analytics, and internal expertise. However, agent-in-the-loop solutions must address the same set of decision-making challenges that AI systems tackle independently—commanding consideration during the architecture design phase, especially in data products integrated for concurrent consumption within enterprises. Within enterprise data products, agentic AI in support of strategic decision-making exposes tertiary product dimensions that determine the product's trustworthiness, its behavioral alignment with user expectations, the underlying technical infrastructure, and the managed change required for adoption and operation. It serves as a lens for examining enterprise data products structured for concurrent syndication, spotlighting product characteristics that ensure decision-making processes yield agent-in-the-loop systems of record supported by AI-assistive technologies and the agents' governing processes. The approach follows convergence techniques mirrored in industries such as cybersecurity that also underpin enterprise data products serving strategic decision-making in the insurance and retirement spaces.
Topology-Driven Neural Defect Pattern Discovery in Silicon Wafers
Semiconductor wafers undergo regular pattern inspections that are essential for assessing production quality. Recent years have seen the rise of deep-learning techniques for automating pattern recognition. They remain underexplored, however, for specific inspection tasks such as clustering similar defect patterns. TDA-based methods have been proposed that utilize TDA to delimit defect patterns and a Gaussian mixture model to perform clustering. A deep neural network is leveraged for the underlying representation learning. A recent dataset of defect pattern images is explored to enable better recognition of such inspections. Results indicate that the method outperforms several baseline methods in clustering accuracy across multiple scenes. Semiconductors perform information acquisition, processing, transmission, and control, and are called the “brain” of electronics. They experience regular pattern inspections at each phase of the manufacturing process, with the inspection results serving as important references for process evaluation and control. Take wafer patterns as an example: if a pattern has serious defects, it cannot be used in subsequent manufacturing processes and must be discarded. Therefore, fast and effective inspection of wafer patterns is important for determining product quality. Recently, deep-learning techniques have been introduced to automate the process of inspection pattern recognition. These tools have been successfully applied in many fields, but they have not yet been fully applied to specific semiconductor inspection tasks such as clustering the same type of defects. TDA-based methods have been proposed that first employ TDA to delimit defect patterns and then a Gaussian mixture model to perform clustering on the defect patterns for a given annealing result.
Intelligent Semantic Communication for 6G-Enabled Industrial Automation
Emerging Agentic AI-driven Semantic Communication for Next-generation 6G Wireless Networks in an Era of Industry Automation The hyper-connected era of Industry Automation (4.0 to 5.0) relies on machinery, agents, and humans continuously exchanging messages using traditional formats with no regard for semantic content. Messages ultimately serve as means to an end (agentic purpose) and are sent to support an ulterior task. However, it has yet to be sufficiently imperative in concrete deployments. Yet, neural networks are undergoing rapid progress towards novel robotics automation/machine learning architectures supporting such structured systems, implementing structured simulated operational environments supporting on-device long-term learning under privacy constraints. Highly demanding and varied communication requirements within dedicated network slices and a distributed Edge-cloud infrastructure enable ultra-low latency, extreme reliability, data assurance processes, strong privacy safeguards, and other critical elements for aspect topics including, but not limited to, security, vulnerability and attack detection, trust establishment, data ownership, and data-sharing. Yet, such demands remain a challenge (if not impossible) to be safely tested and validated in the real world prior to implementation. Hence, a combination of predictive maintenance based on high-quality digital twins and sophisticated sensor-fusion algorithms utilising VR/AR technologies represents a valid solution.
Predictive AI for Alzheimer's Onset and Patient Management
Aging is the most significant independent risk factor for Alzheimer’s disease (AD). Consequently, aging populations are witnessing increased AD incidence, elevating futures of cognitive decline in a growing segment of society. Predictive analytic tools are emerging to support earlier diagnosis, targeting therapeutics when they have the highest efficacy potential, and improved management, easing the care burden on both family and healthcare systems. Advances in AI/machine learning improve prediction accuracy, offering risk scores and other insights to inform practice, allocate care resources efficiently, and guide the creation of personalized treatment plans. At the same time, patient-generated health data are accumulating rapidly, enhancing screening, clinical trial recruitment, and longitudinal monitoring. Automated Natural Language Processing (NLP) methods applied to electronic health record (EHR) data can also improve prediction prevalence and granularity. Growing EHR volume combined with augmented risk modelling enables development of predictive models for rarer conditions such as frontotemporal dementia and Alzheimer’s disease with frontal lobe involvement. Cognitive decline, dementia, or Alzheimer’s disease diagnosis within 3 years can be subjected to risk scoring. NLP can predict correspondingly non-Alzheimer’s disease dementia, vascular dementia, or frontotemporal dementia diagnosis on a similar timeframe.
Transparent ML for Fairer Auto Insurance
Auto insurance underwriting aims to assess applicants' risk and set appropriate insurance premiums. Beyond accurate predictions, underwriting models must satisfy additional requirements to support transparency and fairness—increasingly relevant issues in light of regulatory scrutiny. Should risk assessment help customers better understand the potential for damage or loss, explainable artificial intelligence (XAI) methods are a promising way to enable stakeholders to transparently grasp the model's underlying rationale. The proposed explanatory approach focuses on comparing the outcomes of commonly used predictive models (XGBoost, logistic regression, random forests, support vector classification, and multilayer perceptrons) with those of well-known XAI methods (LIME and SHAP) to assess the trade-off between predictive performance and interpretability. Classification fairness is evaluated across sensitive demographic attributes: age, gender, marital status, and zip code. Applying fairness definitions from the literature highlights cases of unequal treatment for sub-demographic groups. Results demonstrate that the best-performing non-explainable machine learning model for risk assessment—in this case, an XGBoost model—offers the least predictive capability considering the fairness trade-off when compared with its LIME and SHAP explanations. Nevertheless, these XAI techniques present their own disadvantageous trade-offs—namely, an overall drop in performance, predictive quality, and decision-making capability—compared with simpler, yet inherently more interpretable models such as logistic regression and support vector classification. Addressing such trade-offs demands caution and context consideration, yet the application of XAI techniques to support explanations to non-designated recipients remains advantageous, especially in modeling domains in which consumer protection and risk understanding are desired outcomes.