Abstract: Addressing the core problem of insufficient trustworthiness in industrial fault diagnosis, stemming from the limitations of existing methods -- both traditional and deep learning - based -- in terms of interpretability, generalization, and uncertainty quantification, this paper proposes a trustworthy industrial fault diagnosis architecture, the Hierarchical Cognitive Arbitration Architecture (HCAA), which integrates probabilistic models with Large Language Models (LLMs). The architecture conducts a preliminary analysis via a diagnostic engine based on a Bayesian network and features an LLM - driven cognitive arbitration module with multimodal input capabilities. This module performs expert - level arbitration on the initial diagnosis by analyzing structured features and diagnostic charts, holding the priority to make the final decision upon detecting conflicts. To ensure the reliability of the system's output, the architecture integrates a confidence calibration module based on Temperature Scaling and a risk assessment module, which objectively quantify system trustworthiness using metrics like Expected Calibration Error (ECE). Experimental results on a dataset containing multiple fault types demonstrate that the proposed framework improves diagnostic accuracy by over 28 percentage points compared to baseline models, while the post - calibration ECE is reduced by more than 75%. Case studies confirm that the HCAA effectively corrects misjudgments from traditional models caused by complex feature patterns or knowledge gaps, providing a novel and practical engineering solution for building high - trust, explainable AI diagnostic systems for industrial applications. Keywords: Industrial Fault Diagnosis; Large Language Model (LLM); Hierarchical Cognitive Arbitration; Probabilistic Model; Confidence Calibration; Trustworthy AI 1. Introduction With the deep development of Industry 4.0 and smart manufacturing concepts, modern industrial systems are evolving towards high levels of automation and intelligence. In this process, the reliability and safety of equipment have become key factors determining production efficiency and operational costs. Prognostics and Health Management (PHM), as a core technology, plays an indispensable role in improving equipment reliability, reducing unplanned downtime, and optimizing maintenance costs by monitoring equipment status in real - time, diagnosing potential faults, and predicting remaining useful life [1], [2].

The proper use of standard classifications, such as the International Classification of Diseases (ICD) and coding of morbidity data has always been fundamental for all general epidemiological and many health-management purposes (WHO, 2016). One example is the use of the information flow of the Hospital Discharge Records (SDO) collected in national databases for monitoring hospitalization episodes provided in public and private hospitals and thus the provision of hospital assistance. This has become an indispensable tool for both administrative analyses (i.e., for accurate billing) and clinical elaborations (e.g., health quality assessment), which can bring to the planning of new measures to support healthcare and welfare activities or to more strictly clinical-epidemiological and outcome analyses. In this frame, although approaches to coding vary across institutions, clinical coding specialists frequently perform coding retrospectively. The assignment of codes to each patient episode of care during hospitalization is determined by different factors, among others by the coder's interpretation of the available case notes or the completeness of the electronic health records. As a result, accurate coding is dependent on both the intelligibility of the case notes and the coders' knowledge of medical terminology (Sundararajan et al. 2015). Several studies have indicated poor reproducibility of clinical coding (Tatham A., 2008) and poor accuracy which seems not dependent on the version of the standard coding system used, which in the case of SDO is ICD (Quan et al. 2014). In recent years, even if the application of artificial intelligence (AI) has begun to attract and, in some cases, assist clinicians in the practice of medical coding, the performances achieved by AI models do not meet expectations.

Autonomous vehicles require highly sophisticated decision-making to determine their motion. This paper describes how such functionality can be achieved with a practical rule engine learned from expert driving decisions. We propose an algorithm to create and maintain a rule-based behaviour planner, using a two-layer rule-based theory. The first layer determines a set of feasible parametrized behaviours, given the perceived state of the environment. From these, a resolution function chooses the most conservative high-level maneuver. The second layer then reconciles the parameters into a single behaviour. To demonstrate the practicality of our approach, we report results of its implementation in a level-3 autonomous vehicle and its field test in an urban environment.

The spread of the Internet of Things (IoT) is demanding new, powerful architectures for handling the huge amounts of data produced by the IoT devices. In many scenarios, many existing isolated solutions applied to IoT devices use a set of rules to detect, report and mitigate malware activities or threats. This paper describes a development environment that allows the programming and debugging of such rule-based multi-agent solutions. The solution consists of the integration of a rule engine into the agent, the use of a specialized, wrapping agent class with a graphical user interface for programming and testing purposes, and a mechanism for the incremental composition of behaviors. Finally, a set of examples and a comparative study were accomplished to test the suitability and validity of the approach. The JADE multi-agent middleware has been used for the practical implementation of the approach.

The increasing number of connected devices and the complexity of Internet of Things (IoT) ecosystems are demanding new architectures for managing and securing these networked environments. Intrusion Detection Systems (IDS) are security solutions that help to detect and mitigate the threats that IoT systems face, but there is a need for new IDS strategies and architectures. This paper describes a development environment that allows the programming and debugging of distributed, rule-based multi-agent IDS solutions. The proposed solution consists in the integration of a rule engine into the agent, the use of a specialized, wrapping agent class with a graphical user interface for programming and debugging purposes, and a mechanism for the incremental composition of behaviors. A comparative study and an example IDS are used to test and show the suitability and validity of the approach. The JADE multi-agent middleware has been used for the practical implementations.

Complementary and alternative medicine are commonly used concomitantly with conventional medications leading to adverse drug reactions and even fatality in some cases. Furthermore, the vast possibility of herb-drug interactions prevents health professionals from remembering or manually searching them in a database. Decision support systems are a powerful tool that can be used to assist clinicians in making diagnostic and therapeutic decisions in patient care. Therefore, an original and hybrid decision support system was designed to identify herb-drug interactions, applying artificial intelligence techniques to identify new possible interactions. Different machine learning models will be used to strengthen the typical rules engine used in these cases. Thus, using the proposed system, the pharmacy community, people's first line of contact within the Healthcare System, will be able to make better and more accurate therapeutic decisions and mitigate possible adverse events.

AI can easily identify background and mask it out thanks to image segmentation. AI driven recommendations is another classic problem where otherwise people had to put together excel sheets of similar products / complementary products. Some ecommerce brands on shopify and magento platform create and upload these product bundles manually to this day! They can surely get benefitted by such tech. There are extremely complex tasks like autonomous driving which can also be defined as cognitive automation. A driver is taking bunch of known decisions like controlling the speed and direction of the vehicle based on visual input from roads. In case of autonomous driving, the variables are quite large and hence the AI models are going to be lot more complex. But, smaller companies in other industries can greatly benefit from the same technology to provide immediate value to their customers.

In recent years, medical resources in China have been in a state of short supply. Doctors from the top hospitals in modern cities have to face tedious consultation or surgical work every day. Meanwhile, some doctors also need to do scientific research related work for the development of medicine. However, in doctors' daily work, a large part of the work is cumbersome but easy to use IT systems to improve efficiency, such as the management of medical terms, the collection of medical knowledge, and so on. The organization of medical knowledge is mostly for daily diagnosis and treatment in accordance with certain gold standards or guidelines, and many of them are based on some established rules to make inference.

A regular message in this column is that artificial intelligence (AI) won't spread widely until it's easier to use than the requirement to have programmers who can work at the model level. That challenge won't be solved instantly, and it's slowly changing. While technical knowledge is still too often required, there are ways in which development time can be shortened. One way that's been happening has been is the increased availability of pre-built models. A few years back, a tech CEO loved to talk about the "Cambrian Explosion" of deep learning models, as if a lot of models meant real progress in the business world.

A regular message in this column is that artificial intelligence (AI) won't spread widely until it's easier to use than the requirement to have programmers who can work at the model level. That challenge won't be solved instantly, and it's slowly changing. While technical knowledge is still too often required, there are ways in which development time can be shortened. One way that's been happening has been is the increased availability of pre-built models. A few years back, a tech CEO loved to talk about the "Cambrian Explosion" of deep learning models, as if a lot of models meant real progress in the business world.