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Modern concepts in concrete engineering – about the application of artificial intelligence methods The application of artificial intelligence methods provides manifold and promising capabilities to develop innovative concepts in science and practice. Selected methods and modern concepts from the field of concrete construction are presented to outline achievable possibilities. Adaptive prestressed concrete structures were realized using Fuzzy Logic‐based self‐tuning closed‐loop control algorithms. A monitoring concept for continuous structural health monitoring of bridges was elaborated that comprises a model adaptation through evolutionary algorithms and artificial neural networks for reliability validations. For an efficient support of preliminary building design, a knowledge‐based system has been developed that enables the formalization and provision of engineering knowledge through crisp and fuzzy knowledge bases and possibility theory. These modern concepts in concrete construction underline substantial potentials that are achievable through the application of artificial intelligence methods. [ABSTRACT FROM AUTHOR]

Dealing with uncertain, contradicting, and ambiguous information is still a central issue in Artificial Intelligence (AI). As a result, many formalisms have been proposed or adapted so as to consider non-monotonicity. A non-monotonic formalism is one that allows the retraction of previous conclusions or claims, from premises, in light of new evidence, offering some desirable flexibility when dealing with uncertainty. Among possible options, knowledge-base, non-monotonic reasoning approaches have seen their use being increased in practice. Nonetheless, only a limited number of works and researchers have performed any sort of comparison among them. This research article focuses on evaluating the inferential capacity of defeasible argumentation, a formalism particularly envisioned for modelling non-monotonic reasoning. In addition to this, fuzzy reasoning and expert systems, extended for handling non-monotonicity of reasoning, are selected and employed as baselines, due to their vast and accepted use within the AI community. Computational trust was selected as the domain of application of such models. Trust is an ill-defined construct, hence, reasoning applied to the inference of trust can be seen as non-monotonic. Inference models were designed to assign trust scalars to editors of the Wikipedia project. Scalars assigned to recognised trustworthy editors provided the basis for the analysis of the models' inferential capacity according to evaluation metrics from the domain of computational trust. In particular, argument-based models demonstrated more robustness than those built upon the baselines despite the knowledge bases or datasets employed. This study contributes to the body of knowledge through the exploitation of defeasible argumentation and its comparison to similar approaches. It provides publicly implementations for the designed models of inference, which might be a useful aid to scholars interested in performing non-monotonic reasoning activities. It adds to previous works, empirically enhancing the generalisability of defeasible argumentation as a compelling approach to reason with quantitative data and uncertain knowledge. • Replicable comparison of non-monotonic reasoning approaches using quantitative data. • Inference of the ill-defined construct of computational trust using real-world data. • Defeasible argumentation presented more robust inferences. • Literature review on knowledge-based systems and non-monotonic extensions. • Robust results analysed with two large real-world datasets and two knowledge-bases. [ABSTRACT FROM AUTHOR]

Recommender systems predict the requirements and interests of users-based on historical data. All the users having same liking for an item are clustered in the same group through which the recommender system identifies the strengths or pertinent likelihood of an item/product. Different techniques are used to predict the user preferences such as content-based system, collaborative-based filtering, knowledge-based system, multi-attribute trader and e-commerce. Among the several proposed techniques presented in the literature, the content-based and collaborative approaches are the most efficient and effective techniques. These techniques are used in all the hybrid techniques. The content-based identify the description of items and collaborative techniques collect the same preference of users in the same cluster or group. In this paper, we present a survey of recommender systems proposed in the contemporary literature. We have studied the various techniques being used in the recommendation systems and have evaluated them critically by finding their strengths and weaknesses. We have also identified the research gaps related to these techniques and have also suggested the probable improvements in the proposed techniques to make them more effective The critical analysis of the recommendation techniques is presented in this paper. [ABSTRACT FROM AUTHOR]

Computer-based medical diagnosis expert systems are considered both accurate and educationally helpful in most cases. Dizziness and vertigo are among the most common complaints however E.N.T. surgeons and neuro-otologists are not available in the peripheral areas. Computer-Aided Diagnosis In NeurOtology (CADINO) can be of immense value for these unprivileged dizzy patients of remote and rural areas. The study aimed to document the strength, weaknesses and capabilities of CADINO in terms of accuracy, educational usefulness, functionality and effectiveness. Design Hospital-based observational study of a diagnostic tool. Settings Otorhinolaryngology Department of a tertiary care medical college hospital. This prospective study was conducted in 70 patients, 24 simulated cases and 6 case reports from journals. The study included even the feedback of the clinicians before and after consultation. Eleven ENT residents, 14 ENT surgeons [8 teachers and 6 consultants] participated in the study. The overall diagnostic accuracy of the CADINO was found 86%. While in the patients, CADINO accuracy was found 84% approximately similar to faculties/consultants (80%) but it was significantly better than that of residents (57%). Most of the clinicians (84%), rated the CADINO consultation as being educationally helpful, and useful for patient management. CADINO was found very effective and convenient as it could be operated in the OPD simultaneously while evaluating the dizzy patients. CADINO provided accurate diagnostic suggestions. It was found improving patient safety and quality of care by enhancing knowledge and cognitive skills of the clinicians. [ABSTRACT FROM AUTHOR]

Background: Clinical practice guidelines are statements which are based on the best available evidence, and their goal is to improve the quality of patient care. Integrating clinical practice guidelines into computer systems can help physicians reduce medical errors and help them to have the best possible practice. Guideline-based clinical decision support systems play a significant role in supporting physicians in their decisions. Meantime, system errors are the most critical concerns in designing decision support systems that can affect their performance and efficacy. A well-developed ontology can be helpful in this matter. The proposed systematic review will specify the methods, components, language of rules, and evaluation methods of current ontology-driven guideline-based clinical decision support systems. Methods: This review will identify literature through searching MEDLINE (via Ovid), PubMed, EMBASE, Cochrane Library, CINAHL, ScienceDirect, IEEEXplore, and ACM Digital Library. Gray literature, reference lists, and citing articles of the included studies will be searched. The quality of the included studies will be assessed by the mixed methods appraisal tool (MMAT-version 2018). At least two independent reviewers will perform the screening, quality assessment, and data extraction. A third reviewer will resolve any disagreements. Proper data analysis will be performed based on the type of system and ontology engineering evaluation data. Discussion: The study will provide evidence regarding applying ontologies in guideline-based clinical decision support systems. The findings of this systematic review will be a guide for decision support system designers and developers, technologists, system providers, policymakers, and stakeholders. Ontology builders can use the information in this review to build well-structured ontologies for personalized medicine. Systematic review registration: PROSPERO CRD42018106501 [ABSTRACT FROM AUTHOR]

Diabetes is a non-communicable disease that has reached epidemic proportions, affecting 537 million people globally. Artificial Intelligence can support patients or clinicians in diabetes nutrition therapy – the first medical therapy in most cases of Type 1 and Type 2 diabetes. In particular, ontology-based recommender and decision support systems can deliver a computable representation of experts' knowledge, thus delivering patient-tailored nutritional recommendations or supporting clinical personnel in identifying the most suitable diet. This work proposes a systematic literature review of the domain ontologies describing diabetes in such systems, identifying their underlying conceptualizations, the users targeted by the systems, the type(s) of diabetes tackled, and the nutritional recommendations provided. This review also delves into the structure of the domain ontologies, highlighting several aspects that may hinder (or foster) their adoption in recommender and decision support systems for diabetes nutrition therapy. The results of this review process allow to underline how recommendations are formulated and the role of clinical experts in developing domain ontologies, outlining the research trends characterizing this research area. The results also allow for identifying research directions that can foster a preeminent role for clinical experts and clinical guidelines in a cooperative effort to make ontologies more interoperable – thus enabling them to play a significant role in the decision-making processes about diabetes nutrition therapy. • Ontologies enable semantic interoperability and transparent reasoning in AI systems. • Ontology-based systems can support diabetes nutrition therapy via recommendations. • This review studies how diabetes is modelled and how recommendations are generated. • Main trends: use of guidelines, lack of reuse and patterns, scarce collaboration • Experts' knowledge usage, reuse, and alignment can foster ontology-based AI systems. [ABSTRACT FROM AUTHOR]

AI‐methods for the integration of structural design knowledge in early phases of the building design process The early integration of the structural design expertise in the building planning process allows an efficient support of the involved highly complex decision‐making. A knowledge‐based system (KBS) is developed for the supply and the usage of suitable engineering experience knowledge. This performs an evaluation of bearing structures and the proposal of design options through the application of development level dependent fuzzy knowledge bases and related inference systems. The levels of development include the assessment of structures based on the possibility theory and comprises concepts of adaptive detailing. An imitation of the human decision‐making behavior is achieved by the use of an easily understandable formulation of rules following the Modus Ponens and Fuzzy Logic inference mechanisms. Acquisition of applicable knowledge is based on parametric studies covering the calculation and design of structural elements that initially are carried out for reinforced concrete components. Considerable value ranges are determined based on practical experiences. Additionally, mechanisms for the consideration of uncertain parameters are provided. The resulting (KBS) system enables an efficiency increase in the early planning process. [ABSTRACT FROM AUTHOR]

HTE 3.0 aims to support clinicians in the detection of patients with dyslipidemia, especially patients with familial hypercholesterolemia (FH), and in the recommendation of personalized lipid‐lowering treatments. The core of HTE 3.0 is a clinical decision support system in which several knowledge‐based systems are serialized: patient detection, therapeutic target setting, personalized treatment assessment, and treatment combination and prioritization, according to different criteria. The experimental evaluation of HTE 3.0 shows that the use of HTE 3.0 would mean increasing the capacity to detect FH by 5.7 times compared with usual clinical practice. Regarding the lipid‐lowering treatment, a comparison of 18 cases among seven lipidologists shows that the differences between treatments provided by HTE 3.0 and human lipidologists are smaller than the differences between human experts. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a knowledge-based system for automatically diagnosing issues in Vehicular ad hoc networks (VANETs). VANETs enable communication between vehicles and infrastructure, enhancing road safety and efficiency through timely information exchange. The proposed system aims to efficiently maintain and ensure the continuity of network service by leveraging innovative pattern recognition methods tailored to VANETs. The automatic diagnosis problem in VANETs involves estimating the operating class of network components based on sensor observations. This entails associating sensor measurements with specific operating modes. By implementing condition-based preventive maintenance procedures, potential component failures can be detected early, mitigating network disruptions. Various approaches, such as expert systems, fault trees, network state models, and statistical learning through pattern recognition, can be employed to address this problem. This paper primarily focuses on the statistical learning approach, where a classification or regression function is learned from a set of examples to assign operation modes to new measurements. It discusses relevant metrics and preprocessing techniques to simplify the decision-making process. The diagnostic system's results are determined based on the formulation of the classification or regression problem. The learning base is constructed, and an appropriate classification method is selected to develop and validate the automatic diagnosis system. While non-parametric models like support vector machines are commonly used, this article emphasizes the significance of considering assumptions and leveraging additional information to enhance performance. It proposes a more specific formalization of the problem, integrating the unique characteristics of VANETs. The contributions of this article revolve around the theory of belief functions, a generative approach, and the utilization of parametric models defined using graphical models. Experimental studies conducted on artificial datasets have demonstrated the benefits of the semi-supervised approach within the context of VANET networks. [ABSTRACT FROM AUTHOR]

This work applies a knowledge modelling approach in the design of a framework for the generation of travel demand for traffic simulation applications. The proposed framework is based on interchangeable modules that integrate the main stages of travel demand modelling supported by an engineered knowledge base. This approach is intended to promote greater behavioural diversity, incorporate more diverse contextual data, facilitate access to online datasets, and support users to undertake and validate investigations across a range of models and implementations. The framework provides the user with direct control to modify the schema, control the selection of data, select alternative modules to execute, and the potential to remotely retrieve data and execute modules. The framework is investigated through a prototype, which generated travel demand across a full day and performed simulation utilising two third-party traffic simulators based on the configuration by the user schema. The problem of travel demand generation was separated into discrete task modules with identification of features for alternative design or further modularisation. The prototype evaluation generated multimode travel demand that was successfully tested on third-party traffic simulators and evidenced the fitness of semantic technologies in building simulator-agnostic interchangeable framework modules that satisfy the need for configurable travel modelling. The findings of the paper also contribute to the understanding of the challenges in utilising Semantic Web technologies for implementing travel demand generation. It is proposed that the framework provides a basis to develop new and existing approaches to travel demand generation to improve modelling outcomes and adoption. [ABSTRACT FROM AUTHOR]

• Application of the Shapley-Shubik index to determine the agents' strength in a dispersed decision-making system. • A new method for generating the local decisions within one cluster. • A new method of determining a set of global decisions. • The results that were obtained were compared with the results that were obtained in the previous papers. In this paper, dispersed knowledge – accumulated in several decision tables is considered. Dispersion of knowledge is not a part of the work of the system. We assume that the knowledge is already in the dispersed form when it provides to the system. An advanced process of detecting the relations between the decision tables and constructing coalitions is used. The purpose of this paper is to use the measure to determine the strength of the coalition. With this method, a simple method of combining vectors of decisions generated based on local decision tables was applied. The purpose of using the Shapley-Shubik index was to reduce the computational complexity compared to the approach proposed in the earlier papers. In this paper, the results of experiments are presented, and the two approaches are compared. Based on these results, some conclusions have been drawn. [ABSTRACT FROM AUTHOR]

Applying Knowledge-Based Systems (KBS) for hazardous medical waste (HMW) are still limited according to knowledge sources. The two mechanisms of the KBS are commonly known as rule-based and case-based, which can be extracted based on existing regulations that are divided into global, national, provincial, and local authorities. This study used Soft System Methodology and regulation approaches to find out the gaps in coordinating and managing HMW in Indonesia. It is used to depict the rule, processes, and stakeholders for identifying the features of the KBS. The study was taken using four-stage of SSM conducted to classify the objects and their feature requirements. The knowledge source was verified and validated using Focus Group Discussion and represented on CATWOE mode. Most of the regulations are not complying with the practicality of the HMW since it is produced until disposal. The methods give an alternative for knowledge engineers to feed the basis of knowledge. [ABSTRACT FROM AUTHOR]

Modern production systems can benefit greatly from integrated and up-to-date digital representations. Their applications range from consistency checks during the design phase to smart manufacturing to maintenance support. Such digital twins not only require data, information and knowledge as inputs but can also be considered integrated models themselves. This paper provides an overview of data, information and knowledge typically available throughout the lifecycle of production systems and the variety of applications driven by data analysis, expert knowledge and knowledge-based systems. On this basis, we describe the potential for combining data analysis and knowledge-based systems in the context of production systems and describe two feasibility studies that demonstrate how knowledge-based systems can be created using data analysis. This article is part of the theme issue 'Towards symbiotic autonomous systems'. [ABSTRACT FROM AUTHOR]

The occurrence of malfunctions, failures, and loss of effectiveness over time is an unavoidable aspect of any industry. In addition, the continuous ability to compete requires the machines to be in the best possible condition with maximum efficiency. In machinery, a good maintenance strategy means better productivity; more reliability and effectiveness; longer service time, and a safe workplace. The goal of the article is to introduce both the main general machine maintenance strategies and main techniques of the knowledge-based approaches in order to build a diagnostic maintenance Expert System, which can be used for all kind of machines. In addition, the article aims to find the most effective strategy to build a maintenance system that monitors marine engines' conditions in real-time and provides the most suitable recommendations based on a knowledge-based approach. Firstly, we briefly introduce different maintenance strategies, then highlight the knowledge-based approaches. After it we demonstrate the possibility of building an Expert System to diagnose different abnormal conditions in marine engines and give a perspective on challenges and potential opportunities. The significance of the article is the elaboration of the Expert System's conception for maintenance of marine engines. [ABSTRACT FROM AUTHOR]

Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation provided by these algorithms enables transparency and explainability, which is necessary for decision making in critical real-world problems. Yet, causal ML has had limited impact in practice compared to associational ML. This paper investigates the challenges of causal ML with application to COVID-19 UK pandemic data. We collate data from various public sources and investigate what the various structure learning algorithms learn from these data. We explore the impact of different data formats on algorithms spanning different classes of learning, and assess the results produced by each algorithm, and groups of algorithms, in terms of graphical structure, model dimensionality, sensitivity analysis, confounding variables, predictive and interventional inference. We use these results to highlight open problems in causal structure learning and directions for future research. To facilitate future work, we make all graphs, models, data sets, and source code publicly available online. [ABSTRACT FROM AUTHOR]

Healthcare 5.0 is a research trend promoting a patient-centric approach leveraging Artificial Intelligence (AI)-based solutions. It aims to enhance care and quality of life for all patients, including those with a disability. However, when applied to the health sector, AI may be perceived as not transparent: the Explainable AI (xAI) paradigm attempts to solve this issue by providing more understandable, reliable, and human-interpretable AI-based applications. In a field such as disability – characterized by various impairments, limitations in performing activities, or other kinds of restrictions – the possibility to rely on computable representations of domain knowledge in the form of ontologies can support the development of xAI for healthcare. This work proposes a systematic literature review to identify which disabilities are currently represented in domain ontologies, examining which applicative contexts the ontologies were developed for. This review also investigates how the domain ontologies are modelled, underlining several relevant aspects that may foster their adoption in xAI systems. The review process results allow for shedding light on the main disabilities represented in ontologies, tracing the research trends that were at the basis of their development. Results also enable the identification of research lines that can support semantic interoperability – thus enabling ontologies to play a significant role in explaining decision processes performed by AI-based systems in healthcare. [ABSTRACT FROM AUTHOR]

Specifying assembly tasks in computer-aided design (CAD) level is a promising approach to intuitively program complex robot skills. In this article, a three-layered system architecture is presented to generate sensor-based robot skills from an assembly task instance. The architecture consists of an application layer where the user instantiates assembly tasks by specifying CAD constraints between geometric primitives pairs. A process layer infers the most suitable robot skills and their appropriate parameters. This inference is made possible by reasoning on a knowledge database represented as an ontology. The ontology contains semantic models of relevant classes such as tasks, skills, and geometric primitives as well as the relations between them. A control layer executes the sensor-based skills in real time using the eTaSL programming framework. A software implementation for the three layers is presented. The application layer is implemented in FreeCAD, whereas the process layer consists of a Web ontology language (OWL) ontology, a Prolog-based reasoner, and fuzzy inference to correctly select the skill and generate its parameters. In the control layer, the instantiated eTaSL skills execute the assembly tasks by sending an optimized control command to the robot. The system is validated on two challenging assembly cases with two distinct robot types, thus demonstrating the system’s capability across different scenarios. Note to Practitioners—The widespread use of computer-aided design (CAD) models for describing parts assembly has motivated the research community to create systems that automatically generate robot programs satisfying the assembly goal. While most of the existing literature focuses on generating the assembly sequence, this article deals with the aspect of translation from CAD-level assembly specification to executable robot motion, also called skills. This article systematically addresses the problem by dividing it into different layers and solving them separately. Parameters that influence the successful execution of an assembly task are identified and categorized into application- and process-related parameters. Different inference techniques are employed to address each parameter category. Experimental results show that the proposed system can successfully generate and execute robot skills for assembly scenarios of an air compressor and an electric motor. [ABSTRACT FROM AUTHOR]

This research describes the process of collaborative knowledge construction by virtual students of Psychology, by means of a synchronous diagrammatic representation tool. In this mixed study, students made an argument to diagrammatically represent a solution. Students gathered in groups. The analysis consisted in identifying the phases for the construction of knowledge, following these steps: a) organization based on the understanding of the case and agreements to participate, b) exchange of information and argumentation, c) elaboration of constructs and approval of meanings, and d) diagramming the solution of the case. The results show the convenience of a collaborative synchronic tool, because it offers students real-time interaction through argumentation, considering the implications of management in a virtual environment, as well as the formative intent and feedback given by the tutor. The results also show that students are interested in organizing activities, agreeing and abiding by established rules, exchanging information and elaborating meanings to produce knowledge through diagrams from the argumentation. It can be concluded that students were able to establish patterns of interaction derived from the level of involvement and the depth of dialogue during the assignment. Finally, proposals are made for teacher intervention and improvement of collaborative processes. [ABSTRACT FROM AUTHOR]

Architecture, Engineering and Construction or generally construction industry has recently been characterized by increased requirements for information and communication technologies and knowledge systems. All participants of a construction project, from architect through contractor to investor require a high level of automatization. This results in pressure and increased demand for the use of knowledge systems that streamline these processes. This research discusses the issue of the knowledgebased system used in architecture, engineering and construction field. The main aim of the research was to analyze knowledge-based systems used in architecture, engineering and construction and find the overview of exploitation level in this construction sector. The research sample includes all significant participants of the construction project from designer to investor. All research samples were tested by statistical tests and all the data coming from the construction project are realized in Slovakia. [ABSTRACT FROM AUTHOR]

This paper proposes a computationally inexpensive algorithm that utilizes player data to optimize nonplayer character pathfinding in a competitive, multiplayer environment and focuses on imitating the player behavior. The algorithm's input consists of player statistics gathered during the current and previous matches with additional time and space context, similar in design to influence maps. The input is then enriched with two additional, novel variables, allowing easy online fine-tuning of the output. The obtained result influences the final edge values of the map graph. Any known pathfinding algorithm that works with digraphs can then be utilized to control the agent. This paper contains exemplary results obtained when analyzing input on a map modeled after an existing map in the video game Unreal Tournament. [ABSTRACT FROM AUTHOR]

One of the objectives of human resource management (HRM) is to promote the satisfaction of both managers and employees regarding HRM practices. Since promoting satisfaction begins by assessing its current level, this study aims to analyse the challenges faced by industrial companies' managers within developing countries - such as Morocco- in terms of implementing human resource (HR) function practices. This paper presents then a practice-oriented study conducted using a qualitative method based on the content analysis approach. Thus, data collection was based mainly on semi-structured interviews to which 30 managers had participated. Indeed, this study aims to measure managers' satisfaction regarding the HR function while highlighting, at the same time, the practices used in motivation, documentation process, etc., and in terms of locating weaknesses and obstacles to better HRM. The validity of inquiry was tested and proven by two validation strategies: member checking and external audits strategies. The results found, revealed genuinely remarkable weaknesses. Indeed, it turned out to be a lack consistent in terms of the proper structuring of the HR function. In addition to the poor mastery and application of basic HRM practices, namely the primitivity of documentation processes, and the job-person inadequacy, especially in the scarcity of adoption of developed management methods such as the implementation of provisional management of jobs and skills, the establishment of an occupational health system, and maturity in terms of carrying out the tasks of the control system, or even as regards the weaknesses linked to the lack of active communication, to the clarity of the objectives targeted for all the members, and to the sharing of private information, in addition to those arising from the irrelevance of the training received by the staff. As for proposing solutions, the integration of IT tools could improve the performance of HR management processes. Indeed, information systems promote active communication, different storage data that develop the quality of the decision. They also facilitate managing administrative issues, communicating and applying procedures, programming relevant training and motivating employees. The study has important implications for HR professionals and strategic leaders that are especially interested in developing countries. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution begins, and policies are being formulated by economies as well as efforts are being made to meet the needs of the current times. Among other technologies which are part of Fourth Industrial Revolution, big open data is also the part of Pakistan's digital policy framework. Scholarship is available to explore and examine the determinants of BOD diffusion and adoption, a little literature is available to understand big open data as policy innovation and its diffusion. Therefore, this study drew on Diffusion of Innovation (DOI) theory and its application to public policy innovation research to examine patterns of BOD policy innovation diffusion at government and public bodies' level in Pakistan. These patterns are based on policy declaration timing by the governments, policy adoption timing, development of technological applications, and proactively released datasets statistics in public bodies. An Event History Analysis was carried out to examine BOD policy innovation diffusion patterns. Results showed that Federal government is the innovator for the policy innovation diffusion across different governments and subsequent public bodies. It was also found that efficacy ranking of public bodies is quite low in terms of developing BOD technological platforms and proactive release of datasets in large quantities. Therefore, government and policy-makers should focus on effective implementation of big open data policy innovation in Pakistan to equip with 21st century needs. [ABSTRACT FROM AUTHOR]

An ontology-based knowledge representation scheme thrives by establishing relations between different terms belonging to the underlying domain. However, automating the identification of all possible relations that may exist between the different terms pertaining to a vast domain, such as agriculture, is quite challenging. One of the major challenges comes from extracting the inter-subdomain relations which are quite common in the agriculture domain. This present work proposes a knowledge-based scheme for extraction of inter-subdomain relations. In particular, it deals with three such cases, viz. crop and soil, crop and disease, soil and region. A baseline system has been designed by analysing official documents retrieved from different repositories. In an improved version of the proposed scheme co-reference resolution techniques have been used to achieve much higher success with average precision and average recall more than 75% and 85%, respectively. [ABSTRACT FROM AUTHOR]

Despite the recent growing interest in the "factory smartness", still there are only few small and medium enterprises (SMEs) that adopt effective Industry 4.0 (I4.0) solutions. The main reasons can be related to the lack of formalized processes, lack of ICT knowledgeas well as low-cost commercial systems.To cope with these issues, this work focuses on the development and the application of an approach to provide SMEs with a multi-purpose, modular, knowledge-based system: the main aim is to provide a modular and extensible system that can be incrementally implemented without requiring huge initial investments.This system is based on a core design-knowledge meta-model. From this core meta-model, multi-purposes modules can be built: in this paper, we present modules for the traceability support, the AR-powered assembly support, the machine-to-machine control and the data analysis support. [ABSTRACT FROM AUTHOR]

Abstract Background and objectives This work aims to present the expert system EpAssistant, a platform algorithm designed to accurately and automatically identify the EPLETS specificities of anti-HLA (Human Leukocyte Antigen) antibodies in the sera of transplantation candidates. Materials and methods The algorithm was written in the Python™ language and, at this time, integrated with the EpViX system (www.epvix.com.br). EpAssistant uses JSON parameters of the Single-Antigen Bead (SAB) assays from both vendors, the patient's HLA typing and the Cut Off (CO) value as inputs. The outputs of the tool are the EPLETSS explaining the reactivity patterns of anti-HLA antibodies in the patient's serum. Results As preliminary results, we present the development and establishment of the EpAssistant platform. EpAssistant analyses can be performed for Class I (− A, B and C) and Class II (− DR, − DQ and -DP) HLA molecules. Conclusions EpAssistant automates the EPLETS reactivity analysis process and drastically reduces the time required to produce final results, enabling large-scale data analyses in a simple, inexpensive and rapid manner and facilitating the allocation of donated organs via the EPLETS Virtual Crossmatch (EVxM) system. Highlights • A expert system designed to accurately identify the epitope specificities of preformed anti-HLA (Human Leukocyte Antigen) antibodies in an automated manner. • Facilitator of medical decision-making regarding the identification of the best donor for a high-immunologic risk recipient. • A program that automates the analysis process of epitope reactivity and drastically reduces the time required to produce final results. [ABSTRACT FROM AUTHOR]

Common knowledge acquisition and documentation structuring (CommonKADS) methodology is used for building knowledge-based systems. Legacy systems built depending on CommonKADS suffer from weak points regarding reusability. The main objectives of this work are: (1) switching of CommonKADS methodology from just a design model to be an executable application, (2) facilitating the linkage and cooperation between CommonKADS services that are using different terminologies and (3) enhancing suitability and reusability of existing CommonKADS-based systems. An enhancement to the CommonKADS methodology in order to improve its applications reusability is introduced. This enhancement contains an adaptation of the original CommonKADS methodology and utilization of service-oriented architecture (SOA) as a promising software engineering technology. The proposed adaptation model includes two alternative processes: adjusting and converting. An adjusting process performs a transformation of the existing CommonKADS layers to be SOA-enabled so that it includes representing the data in a new standard form to be transformed into set of services. Converting process is concerned with changing CommonKADS-based legacy systems to SOA-enabled systems through using GenericSOA framework. An example application, a potato CommonKADS-based expert system, was used to evaluate the new proposed model through the analysis and automated restructuring of it to be SOA-enabled system. [ABSTRACT FROM AUTHOR]

• Systematic reviews aim to provide high-quality evidence-based syntheses. • They help make informed recommendations for practice/policy but are poorly reported. • Reporting guidelines have been developed to support systematic reviews reporting. • To assure that a review adheres to a reporting guideline, we use assurance cases. • We rely on Expert Systems to implement assurance cases and support decision-making. Systematic reviews aim to provide high-quality evidence-based syntheses for efficacy under real-world conditions and allow understanding the correlations between exposures and outcomes. They are increasingly popular and have several stakeholders (e.g., healthcare providers, researchers, educators, students, journal editors, policy makers, managers) to whom they help make informed recommendations for practice or policy. Systematic reviews usually exhibit low methodological and reporting quality. To tackle this, reporting guidelines have been developed to support systematic reviews reporting and assessment. Following such guidelines is crucial to ensure that a review is transparent, complete, trustworthy, reproducible, and unbiased. However, systematic reviewers usually fail to adhere to existing reporting guidelines, which may significantly decrease the quality of the reviews they report and may result in systematic reviews that lack methodological rigor, yield low-credible findings and may mislead decision-makers. To assure that a review complies with reporting guidelines, we rely on assurance cases that are an emerging way of arguing and relaying various safety–critical systems' requirements in an extensive manner, as well as checking the compliance of such systems with standards to support their certification. Since the nature of assurance cases makes them applicable to various domains and requirements/properties, we therefore propose a new type of assurance cases called systematicity cases. Systematicity cases focus on the systematicity property and allow arguing that a review is systematic i.e., that it sufficiently complies with the targeted reporting guideline. The most widespread reporting guidelines include PRISMA (Preferred Reporting Items for Systematic reviews and meta-Analyses). We measure the confidence in a systematicity case representing a review as a means to quantify the systematicity of that review i.e., the extent to which that review is systematic. We rely on rule-based Artificial Intelligence to create a knowledge-based system that automatically supports the inference mechanism that a given systematicity case embodies and that allows making a decision regarding the systematicity of a given review. An empirical evaluation performed on 25 reviews (self-identifying as systematic) showed that these reviews exhibit a suboptimal systematicity. More specifically, the systematicity of the analyzed reviews varies between 32.96% and 66.49% and its average is 54.42%. More efforts are therefore needed to report systematic reviews of higher quality. More experiments are also needed to further explore the factors hindering and/or assuring the systematicity of reviews. The main beneficiaries of our work are journal reviewers, journal editors, managers, policymakers, researchers, organizations developing reporting guidelines, peer reviewers, students, insurers, evidence users, as well as reporting guidelines developers. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, flipped learning has received tremendous attention from educational practitioners and researchers. However, this study argues that existing e‐learning systems mainly serve for learning management and content delivery purposes, although they lack support for flipped learning. As an innovative educational approach, flipped learning requires more pedagogical elements, such as integrated instructional design and adaptive content delivery, to achieve effective direct instruction. This study aims to create a learning adaptivity design to support effective learning in the flipped individual learning space in which the teacher is absent. Because teaching involves various pedagogical and content knowledge sources, we propose a conceptual model of teaching as a function of the knowledge triad of Guideline (G), Teaching Activity (T), and Material (M). To realize this conceptualization, an ontological problem‐solving approach is used for knowledge‐based systems development to integrate the relevant knowledge sources. The knowledge model is created using the Protégé platform to develop OWL‐based domain ontology, task ontology, and SWRL‐based semantic rules to enable inference in the GTM triad for learning adaptivity. The case illustration shows that the knowledge‐based system prototype can adaptively guide student learning in the flipped individual learning space with the knowledge sources integrated. [ABSTRACT FROM AUTHOR]

Background: Correct clinical management of circulatory shock in emergency departments (ER) and intensive care units (ICU) is critical. In this context, the transmission of professional skills by means of the practical supervision of real cases at the point of care entails important issues that can be widely overcome with the use of computer knowledge-based e-learning tools.Objective: Shock-Instructor is a web-based e-learning tool implementing the already tested training program model (TPM) that uses a knowledge base about the evidence found in the clinical practice guidelines about seven types of shock. This tool is expected to reduce the learning times and to improve the skills of hospital residents with regard to both the correct application of the guidelines and patient recovery, suppressing the risks of direct interventions.Methods: Shock-Instructor has been used to train residents in the Emergency Department of the Hospital Clínic de Barcelona (Spain) in order to reduce the learning cycle without affecting quality. A case-base with the description of 51 cases with shock and a knowledge-base with 137 clinical rules about the treatment of shock were incorporated to the Shock-Instructor system. A group of 33 residents was involved in a randomized controlled trial to check whether the use of Shock-Instructor can significantly improve the skills of clinicians after one week of problem-based training.Results: No significant differences were found in the skill levels of the intervention (IG) and control (CG) groups prior to learning. However, we observed an improvement of the IG clinicians capacity to stabilize patients with shock in better clinical conditions (5% improvement, p = 0.004), and to reduce the risk of death in 19.52% (p = 0.004), after training. First-year residents in IG enhanced 14.3% their sensitivity in the correct application of guidelines (p = 0.01), and 14.9% the mean survival rate of their patients (p = 0.01), after being trained with Shock-Instructor for a week. Residents with specialties different from ER and ICU enhanced 16.1% their application of guidelines (p = 0.04), and 14.5% the survival rate of the cases attended (p = 0.01).Conclusions: Web-oriented knowledge-based e-learning computer tools such as Shock-Instructor can significantly reduce the learning time of ER and ICU residents, while enhancing their assimilation of evidence-based medicine and both the survival rate and health condition of patients with shock. [ABSTRACT FROM AUTHOR]

The enthusiasm for artificial intelligence (AI) as a source of solutions to problems is not new. In law, from the early 1980s until at least the early 2000s, considerable work was done on developing ‘legal expert systems.’ As the DataLex project, we participated in those developments, through research and publications, commercial and non-commercial systems, and teaching students application development. This paper commences with a brief account of that work to situate our perspective. The main aim of this paper is an assessment of what might be of value from the experience of the DataLex Project to contemporary use of ‘AI and law’ by free legal advice services, who must necessarily work within funding and other constraints in developing and sustaining such systems. We draw fifteen conclusions from this experience, which we consider are relevant to development of systems for free legal advice services. The desired result, we argue, is the development of integrated legal decision-support systems, not ‘expert systems’ or ‘robot lawyers’. We compare our insights with the approach of the leading recent text in the field, and with a critical review of the field over twenty-five years. We conclude that the approach taken by the DataLex Project, and now applied to free legal advice services, remains consistent with leading work in field of AI and law. The paper concludes with brief suggestions of what are the most desirable improvements to tools and platforms to enable development of free legal advice systems. The objectives of free access to legal information services have much in common with those of free legal advice services. The information resources that free access to law providers (including LIIs) can provide will often be those that free legal advice services will need to use to develop and sustain free legal advisory systems. There is therefore strong potential for valuable collaborations between these two types of services providers. [ABSTRACT FROM AUTHOR]

Crowdsourcing has become an important problem-solving technique, for both traditional and mobile applications. There have been work on crowdsourcing particular database operations to humans, and work on spatial crowdsourcing or mobile crowdsourcing, where the workers are mobile device users. This paper presents LogicCrowd which extends logic programming, commonly used for knowledge-based applications, with crowdsourcing capabilities by adding operators to connect to crowdsourcing platforms, thereby enabling crowdsourcing via logic programs and crowd-powered knowledge-based applications. In LogicCrowd, we also introduce a novel unification approach called crowd unification that automatically leverages human knowledge for comparisons through the crowdsourcing paradigm. In addition, LogicCrowd is built on a Prolog platform running on Android mobile devices, thereby enabling logic-based spatial and mobile crowdsourcing. Our proposed method for combining rule-based reasoning and crowdsourcing via LogicCrowd programs is demonstrated in a range of scenarios. Because energy is an important consideration for mobile platforms, we also investigate the energy characteristics of the crowdsourcing operators in our LogicCrowd prototype. Our experiments show the relationships between crowdsourcing operations and energy consumption, and illustrate the factors influencing energy consumption when using crowdsourcing. [ABSTRACT FROM AUTHOR]

People have many constraints concerning the food they eat. These constraints can be based on religious believes, be due to food allergies or illnesses, or be derived from personal preferences or dislikes. For instance, preparing the menus at a hospital can be really complex. Another special situation arises when traveling abroad or simply when eating at a foreign cuisine restaurant (it is not always enough to know the brief description in the restaurant’s menu or the explanation of the waiter). Therefore, we consider that it would be very interesting to develop a knowledge-based system that automatically obtained a personalized menu for each customer, according to the precise recipes of the restaurant and taking into account the data given by the customer and the ingredients out of stock (if any). Although there are many knowledge-based systems devoted to diabetic’s meals, diets, food supply chains, etc., we do not know of any comparable system. We have developed a rule based expert system that uses sets and lists for handling data and an algebraic inference engine. It has been implemented in the computer algebra system Maple . [ABSTRACT FROM AUTHOR]

The journal Knowledge-based Systems (KnoSys) has been published for over 25 years, during which time its main foci have been extended to a broad range of studies in computer science and artificial intelligence. Answering the questions: “What is the KnoSys community interested in?” and “How does such interest change over time?” are important to both the editorial board and audience of KnoSys. This paper conducts a topic-based bibliometric study to detect and predict the topic changes of KnoSys from 1991 to 2016. A Latent Dirichlet Allocation model is used to profile the hotspots of KnoSys and predict possible future trends from a probabilistic perspective. A model of scientific evolutionary pathways applies a learning-based process to detect the topic changes of KnoSys in sequential time slices. Six main research areas of KnoSys are identified, i.e., expert systems, machine learning, data mining, decision making, optimization, and fuzzy, and the results also indicate that the interest of KnoSys communities in the area of computational intelligence is raised, and the ability to construct practical systems through knowledge use and accurate prediction models is highly emphasized. Such empirical insights can be used as a guide for KnoSys submissions. [ABSTRACT FROM AUTHOR]

The Automatic Identification System (AIS) is a ship reporting system based on messages broadcast by vessels carrying an AIS transponder. The recent increase of terrestrial networks and satellite constellations of receivers is making AIS one of the main sources of information for Maritime Situational Awareness activities. Nevertheless, AIS is subject to reliability and manipulation issues; indeed, the received reports can be unintentionally incorrect, jammed or deliberately spoofed. Moreover, the system can be switched off to cover illicit operations, causing the interruption of AIS reception. This paper addresses the problem of detecting whether a shortage of AIS messages represents an alerting situation or not, by exploiting the Received Signal Strength Indicator available at the AIS Base Stations (BS). In designing such an anomaly detector, the electromagnetic propagation conditions that characterize the channel between ship AIS transponders and BS have to be taken into consideration. The first part of this work is thus focused on the experimental investigation and characterisation of coverage patterns extracted from the real historical AIS data. In addition, the paper proposes an anomaly detection algorithm to identify intentional AIS on-off switching. The presented methodology is then illustrated and assessed on a real-world dataset. [ABSTRACT FROM AUTHOR]

Purpose -- A wearable expert system (WES) is an expert system designed and implemented to obtain input from and give outputs to wearable devices. Among its distinguishing features are the direct cooperation between domain experts and users, and the interaction with a knowledge maintenance system devoted to dynamically update the knowledge base taking care of the evolving scenario. The paper aims to discuss these issues. Design/methodology/approach -- The WES development method is based on the Knowledge Acquisition Framework based on Knowledge Artifact (KAFKA) framework. KAFKA employs multiple knowledge artifacts, each devoted to the acquisition and management of a specific kind of knowledge. The KAFKA framework is introduced from both the conceptual and computational points of view. An example is given which demonstrates the interaction, within this framework, of taxonomies, Bayesian networks and rule-based systems. An experimental assessment of the framework usability is also given. Findings -- The most interesting characteristic of WESs is their capability to evolve over time, due both to the measurement of new values for input variables and to the detection of new input events, that can be used to modify, extend and maintain knowledge bases and to represent domains characterized by variability over time. Originality/value -- WES is a new and challenging concept, dealing with the possibility for a user to develop his/her own decision support systems and update them according to new events when they arise from the environment. The system fully supports domain experts and users with no particular skills in knowledge engineering methodologies, to create, maintain and exploit their expert systems, everywhere and when necessary. [ABSTRACT FROM AUTHOR]

Research in Expert Systems (ES) has been one of the longest-running, and most successful areas of ongoing research within the AI field. Since the 1980s, many case studies of ES applications have been published covering a wide range of functional areas and problem domains. These case studies contain an enormous amount of information about how ESs have been developed and how the tools, concepts, and applications have evolved since their inception. This research has painstakingly collected and analysed the content of 311 ES case studies dating from 1984 through 2016. A detailed content analysis was performed on this corpus in order to capture as many details as possible from each case. Further value was added to the study by using an impact scale to try and gauge the impact or “success” of the resulting application. With such a large sample size, the results are helpful in identifying how ES research has evolved and areas for further research. [ABSTRACT FROM AUTHOR]

This paper deals with research into advanced hierarchical graph structures which have been developed for knowledge representation in computer-aided design and robotics. Complex real-life engineering problems still need useful graph-based models. A new approach to represent models preserving the intrinsic nature of problem structures using graphs with many hierarchies is proposed. Different types of hierarchical dependencies (geometrical, functional, etc.) between design parts can be expressed in one structure. Moreover, the possibility of selecting only problem-related hierarchies reduces the number of data to be analysed in the process of reasoning about considered designs. This approach is illustrated by applying multi-hierarchical graph structures to represent complex data used for robot control algorithms. [ABSTRACT FROM AUTHOR]

In this paper we propose a two-stage methodology to classify the non-banking financial institutions (NFIs) based on their financial performance. The first stage of the methodology consists of grouping the companies in similar financial performance classes (e.g.: “good”, “average”, “poor” performance classes). We optimise the allocation of the observations within the performance clusters by applying an enhanced version of an observation re-allocation procedure proposed in our previous work. Next, based on the result of the grouping phase, we construct a performance class variable by attaching a performance label to each data row. Then, in the second phase of our methodology, we propose a feed-forward neural-network classification model that maps the input space to the newly-constructed performance class variable. This model allows us to forecast the performance of new companies as data become available. [ABSTRACT FROM AUTHOR]

The paper presents a general concept of smart design and production control as key elements for efficient operation of a smart factory. The authors present various techniques that aid the design process of individualized products and organization of their production in the context of realization of the mass customization strategy, which allows a shortened time of development for a new product. Particular attention was paid to integration of additive manufacturing technologies and virtual reality techniques, which are a base of the so-called hybrid prototyping. [ABSTRACT FROM AUTHOR]

Twitter being among the popular social media platforms, provide peoples' opinions regarding specific ideas, products, services, etc. The large amounts of shared data as tweets can help extract users' sentiment and provide valuable feedback to improve the quality of products and services alike. Similar to other service industries, the airline industry utilizes such feedback for determining customers' satisfaction levels and improving the quality of experience where needed. This, of course, requires accurate sentiments from the user tweets. Existing sentiment analysis models suffer from low accuracy on account of the contradictions found in the tweet text and the assigned label. From this perspective, this study proposes a hybrid sentiment analysis approach where the lexicon-based methods are used with deep learning models to improve sentiment accuracy. Experiments involve analyzing the impact of TextBlob on the classification accuracy of models as against the original annotations, considering that the probability of the false annotations cannot be overlooked. Furthermore, the efficacy of TextBlob against Afinn and VADER (Valence Aware Dictionary for Sentiment Reasoning) is also evaluated. The CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and CNN-LSTM are deployed in comparison with state-of-the-art machine learning models. Additionally, the efficiency and efficacy of TF-IDF (Term Frequency-Inverse Document Frequency) and BoW (Bag of Words) are also investigated. Results suggest that models perform better when trained using the TextBlob assigned sentiments as compared to the original sentiments in the dataset. LSTM-GRU outperforms all models and previous studies with the highest 0.97 accuracy and 0.96 F1 scores. From machine learning models, the support vector classifier and extra tree classifier achieve the highest accuracy score of 0.92, with TF-IDF and BoW, respectively. Despite the good performance of the models using the TextBlob labels, TextBlob-based annotation cannot replace humans. Our stance is that with humans, bias, error-proneness, and subjectivity cannot be ignored; so we propose that the TextBlob-annotated labels can be used as assistance for human annotators where human annotators can wet the TextBlob-annotated dataset. [ABSTRACT FROM AUTHOR]

To protect the health of human and environment, the European Union implemented the REACH regulation for chemical substances. REACH is an acronym for Registration, Evaluation, Authorization, and Restriction of Chemicals. Under REACH, the authorities have the task of assessing chemical substances, especially those that might pose a risk to human health or environment. The work under REACH is scientifically, technically and procedurally a complex and knowledge-intensive task that is jointly performed by the European Chemicals Agency and member state authorities in Europe. The assessment of substances under REACH conducted in the German Environment Agency is supported by the knowledge-based system KnowSEC, which is used for the screening, documentation, and decision support when working on chemical substances. The software KnowSEC integrates advanced semantic technologies and strong problem solving methods. It allows for the collaborative work on substances in the context of the European REACH regulation. We discuss the applied methods and process models and we report on experiences with the implementation and use of the system. [ABSTRACT FROM AUTHOR]

Over the past decades, sensor networks have been deployed around the world to monitor over time and space a large number of properties appertaining to various environmental phenomena. A popular example is the monitoring of particulate matter and gases in ambient air undertaken, for instance, to assess air quality and inform decision makers and the public. Such infrastructure can generate large amounts of data, which must be processed to derive useful information. Infrastructure may be for environmental research, specifically. In order to reduce duplication and improve interoperability, efforts have been initiated more recently that aim at abstract architectural descriptions of infrastructure that supports the acquisition, curation, access, and processing of measurement and observation data. The ENVRI Reference Model (ENVRI-RM) is an example for an abstract architectural description of infrastructure tailored for environmental research. We briefly summarize ENVRI-RM and provide an overview of its subsystems, functionality, and viewpoints. We highlight that its primary focus is on the data life-cycle in environmental research infrastructure. As our contribution, weextend ENVRI-RM with functionality for the acquisition of knowledge from data, and the curation, access, and processing of knowledge. The extension, which we name +K, aims at addressing the knowledge life-cycle in environmental research infrastructure. We present the +K subsystems and functionality, and discuss the extension from ENVRI-RM viewpoints. We argue that the +K extension can be superimposed on ENVRI-RM to form the ENVRI-RM+K model for the 'archetypical' knowledge-based environmental research infrastructure that addresses both data and knowledge life-cycles. We demonstrate the application of the extension to a concrete use case in aerosol science. [ABSTRACT FROM AUTHOR]

Symbolic, subsymbolic, and hybrid approaches to rule extraction have so far relied on subsets of first-order logic to cope with the expressiveness trade-off of knowledge representation, on black-box approaches based on artificial neural networks, or on frequent association rule mining in the knowledge discovery and data mining fields. In this article, we present an entirely new method for rule extraction in knowledge-based systems that consists in retrieving an initial set of rules extracted from a knowledge base using conventional logical approaches and then ranking this initial set of rules applying a psychologically motivated multicriteria decision analysis method. We show how this method can be used to implement a knowledge-based management system, demonstrate that this method outperforms the most efficient algorithms for rule extraction proposed to date in the knowledge representation and knowledge discovery fields, and describe its implementation in a knowledge-based innovation tutoring system. [ABSTRACT FROM AUTHOR]

The power transformer is one of the most critical and expensive equipments in an electric power system. If it is out of service in an unexpected way, the damage for both society and electric utilities is very significant. Over the last decades, many computational tools have been developed to monitor the ‘health’ of such an important equipment. The classification of incipient faults in power transformers via Dissolved Gas Analysis (DGA) is, for instance, a very well known technique for this purpose. In this paper we present an intelligent system based on cognitive systems for fault prognosis in power transformers. The proposed system combines both evolutionary and connectionist mechanisms into a hybrid model that can be an essential tool in the development of a predictive maintenance technology, to anticipate when any equipment fault might occur and to prevent or reduce unplanned reactive maintenance. The proposed procedure has been applied to real databases derived from chromatographic tests of power transformers found in the literature. The obtained results are fully described showing the feasibility and validity of the new methodology. The proposed system can help Transformer Predictive Maintenance programmes offering a low cost and highly flexible solution for fault prognosis. [ABSTRACT FROM AUTHOR]

Unsupervised automatic topic discovery in micro-blogging social networks is a very challenging task, as it involves the analysis of very short, noisy, ungrammatical and uncontextual messages. Most of the current approaches to this problem are basically syntactic, as they focus either on the use of statistical techniques or on the analysis of the co-occurrences between the terms. This paper presents a novel topic discovery methodology, based on the mapping of hashtags to WordNet terms and their posterior clustering, in which semantics plays a centre role. The paper also presents a detailed case study in the field of Oncology, in which the discovered topics are thoroughly compared to a golden standard, showing promising results. [ABSTRACT FROM AUTHOR]

Modern cars have many dashboard lights and not all drivers recognize or know the importance of all of them. Red symbols usually indicate a safety issue or a serious problem, meanwhile yellow symbols use to indicate a not so urgent problem. Green and blue symbols usually provide information about the systems connected. But not all the red icons require of the same action, and the user manuals of most modern cars, with their sophisticated electronic systems, have hundreds of pages. Meanwhile, smart devices have become popular and have an outstanding computing power plus Internet connection. Consequently, the conditions for developing a knowledge-based system that helped the unaware driver in case a dashboard light went on, exist. The application should evaluate the situation and recommend the best actions to be carried out by the driver (regarding the possible repair on site or at a workshop, its urgency or even the need to immobilize the vehicle immediately). We have designed such a knowledge-based system and have developed a simplified one as example. A friendly Graphical User Interface has been developed in order to ease the communication with the application and its use in remote using any smart device with Internet connection. [ABSTRACT FROM AUTHOR]

This work presents a methodology for the selection and comparison of non-traditional sheet metal cutting processes as a new structure of selection by means of an expert system. The model is generated from a knowledge base acquired from diverse experts, and the use of fuzzy logic techniques. With a simple input of the parameters of a piece, the system offers the most appropriate cutting options (based on the requirements of the piece) allowing a non-expert user selecting the most appropriate process with emphasis on a predefined priority: finish, cost or time. The selection process consists of four base algorithms that measure the attributes of each process as a dependent indicator of the other processes, that is, a pre-selection that considers (1) the process capability to cut a material-thickness relation, (2) the speed that can be achieved with this relation, (3) the inherent complexity of the piece to be cut, and (4) the process tolerance. Results of experiments under three different approaches prove that the expert system here presented accurately prioritizes the most convenient cutting processes. [ABSTRACT FROM AUTHOR]