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This study explores the contemporary landscape of integrating numerical algorithms, artificial intelligence (AI), and expert methodologies within the domain of nephrology. Focusing on automation and decision support, we scrutinize the impact of numerical algorithms on the precise evaluation of kidney function. Furthermore, we delve into the transformative potential of artificial intelligence, particularly in the realms of machine learning and deep learning applications, elucidating its role in early disease detection and the formulation of personalized treatment strategies. The synergy between computer-based tools and expert-driven approaches is examined, underscoring their collaborative role in enhancing the accuracy and dependability of diagnoses. Additionally, we address the ethical considerations and challenges associated with the incorporation of automation in nephrology. The paper provides a concise overview of in-depth analysis while illustrating the promising prospects of these innovative methodologies for reshaping nephrology research and patient care.

The manufacturing of hard disk drives involves the intricate assembly of numerous components, making the testing process time-consuming and resource intensive. To optimize the manufacturing process and increase testing efficiency, the development of a rule-based expert system is proposed. This system leverages predictive models constructed from assembly process data to identify potentially defective hard drives before undergoing extensive testing. By preemptively identifying defects, this approach substantially reduces testing time and enhances tester capacity. Given the categorical and imbalanced nature of assembly data, Decision Trees are employed as the prediction model. Specifically, three Decision Tree algorithms are explored: ID3, C4.5, and CART. In addition, four feature selection techniques, namely Information Gain, Gain Ratio, Chi-Square, and Symmetrical Uncertainty, are utilized to identify high-impact features. Our experimental findings reveal that Information Gain coupled with the C4.5 algorithm yields the most favorable results in terms of prediction accuracy, modeling efficiency, and rule generation. Moreover, our study establishes that setting the failure probability threshold between 0.15 and 0.70 provides the shortest total test time for the proposed process, as supported by a 95% confidence level. This achievement represents a statistically significant enhancement compared with the existing manufacturing process.

The paper involved conducting preliminary research to explore the identification and control of a multi-dimensional, non-linear, and non-stationary cement grinding process using artificial neural networks and various other non-linear models. The primary objective was to establish a precise model that accurately characterizes the functioning of the grinding system. Several model structures were employed, including NARX models based on feed-forward network, Elman, Jordan, and Layer-Recurrent Network (LRN) recurrent networks, as well as MTL (Multi-Task Learning) and traditional NARX non-linear models. It was observed that, in contrast to the linear models, the non-linear models exhibited significantly superior performance in the modeling of the system. Another notable outcome of this research is the proposal of a neurocontroller, functioning as an expert system, which can provide control signals to operators. The development and implementation of such a neurocontroller have the potential to enhance the quality, simplicity, and efficiency of cement grinding process control.

This paper investigates a novel fuzzy Petri nets (FPNs) method based on q-rung orthopair fuzzy sets (q-ROFSs) to provide an efficient solution to uncertain knowledge representation and reasoning. It not only improves FPN’s flexibility in knowledge parameter representation and reasoning algorithms but also addresses the challenges that most FPNs cannot implement backward reasoning, which is a common reasoning task to infer condition statuses according to consequences reversely. Specifically, we first propose the q-rung orthopair FPNs (q-ROFPNs) by integrating q-ROFSs with FPNs. It achieves an intuitive evaluation of hesitancy information and a flexible adjustment of the knowledge representation ranges. And a reasoning algorithm based on the ordered weighted averaging-weighted average (OWAWA) operator is developed to accomplish the forward reasoning driven by q-ROFPNs, which can balance the proposition weights and its position weights flexibly. Building upon q-ROFPNs, we further propose the q-rung orthopair fuzzy reversed Petri nets (q-ROFRPNs) for backward reasoning task, where a decomposition algorithm for q-ROFRPNs is designed for reducing the inference complexity, and an ordered weighted backward reasoning (OWBR) algorithm is provided to backward reasoning suitable for different fuzzy environments. In addition, to ensure the accuracy and rationality of reasoning results, we propose a knowledge acquisition method by power average (PA) operator to eliminate the negative impact of outliers on knowledge parameter assessments. A simulation experiment on the fault diagnosis of the air conditioning system demonstrates that the proposed method can achieve a more flexible and reliable knowledge representation and reasoning than the state-of-the-art FPNs methods.

Wind power curve modeling is essential in the analysis and control of wind turbines (WTs), and data preprocessing is a critical step in accurate curve modeling. As traditional methods do not sufficiently consider WT models, this paper proposes a new data cleaning method for wind power curve modeling. In this method, a model-data hybrid-driven (MDHD) outlier detection method is constructed, and an adaptive update rule for major parameters in the detection algorithm is designed based on the WT model. Simultaneously, because the MDHD outlier detection method considers multiple types of operating data of WTs, anomaly detection results require further analysis. Accordingly, an expert system is developed in which a knowl-edgebase and an inference engine are designed based on the coupling relationships of different operating data. Finally, abnormal data are eliminated and the power curve modeling is completed. The proposed and traditional methods are compared in numerical cases, and the superiority of the proposed method is demonstrated.

Modern transient electromagnetic (TEM) surveys, either ground-based or airborne, may yield thousands of line kilometers of data. Parts of these data, especially in areas with dense infrastructure, are often disturbed by electromagnetic couplings due to infrastructure, e.g., power cables and fences. In most cases and in particular when working in a hydro-geological context, such coupled data must be culled before inversion. The process of identifying and culling coupled data is a manual task, requiring specialists to examine and process the data in detail. Manual data processing is subjective, difficult to reproduce, and time-consuming. To automate the complex data processing workflows, we propose an expert system based on a deep convolutional auto-encoder to identify couplings in the data. We configure the auto-encoder to learn an encoded representation of TEM data in a latent space. A reconstruction part that decodes the encoded representation is also trained, aiming to reconstruct input data. If the data unaffected by electromagnetic couplings are observed by the auto-encoder, the reconstructed output will have low error to the input. However, when having couplings in the data, the reconstruction error is elevated, indicating a nongeologic anomaly. The size of the anomaly is based on the relative error between the input data and the reconstructed output normalized by the data standard deviation. We show that the proposed approach displays high-quality data processing within a fraction of a second for a ground-based and an airborne system, which is either ready for inversion or requires minimal further quality inspection. [ABSTRACT FROM AUTHOR]

The complex traffic and road environment pose considerable challenges to the accuracy, timeliness, and adaptive ability of connected and autonomous vehicles (CAVs) in making driving decisions. This paper uses vehicle collaboration and integrates the adaptive learning capabilities of machine learning and the interpretation capabilities of expert systems (ESs) in a unified architecture to form a hybrid autonomous driving guidance system, which not only solves the “bottleneck” of knowledge acquisition during the construction of expert systems but also solves the “black box” phenomenon of machine learning in the decision-making process. First, an autonomous driving strategy based on deep reinforcement learning (DRL) is proposed for CAVs to make decisions and extract corresponding rules. Next, we design an ES knowledge base expansion method including rule extraction, rule sharing, and rule test. Particularly, vehicular blockchain is adopted to ensure user privacy and data security during the rule-sharing process. Third, hybrid autonomous driving guidance combining ES and machine learning is proposed for CAVs to make accurate and efficient decisions in different driving environments. Once the strategy is well trained, it can effectively guide CAVs to cope with the complex traffic environment. Extensive simulations validate the performance of our proposal in terms of decision-making accuracy, effectiveness, and safety. [ABSTRACT FROM AUTHOR]

Harmful algal blooms (HABs) are one of nature’s responses to nutrient enrichment in aquatic systems and increasingly occur in coastal waters, such as in Lampung Bay and Jakarta Bay, Indonesia. HABs present environmental and fisheries management challenges due to their unpredictability, spatial coverage, and detrimental health effects on coastal organisms, including humans. Here, an automated algae species identification system assisted and validated by expert judgment was proposed. The system used ontology as guidance to determine the species of algae and certainty factors to indicate the level of confidence of the experts when providing a statement or judgment for a particular object or event under consideration. The system was tested to identify 60 samples using 51 predetermined algal characteristics. The tests were narrowed down to the 20 most common HAB-causing algae types found in the study sites and compared with identification by experts. The results showed that the system successfully identified the test data with an accuracy of 73.33%. The system also had a high agreement (above 79.75%) with the identification performed by experts on six algae species. Further improvement of the system’s accuracy could facilitate its use as an alternative tool in rapid algal identification or part of an early warning system for HABs.

Fuzzy Petri nets (FPNs) are a useful instrument for modelling expert systems to conduct knowledge representation and reasoning. Many studies have been carried out for improving the performance of FPNs in terms of their accurate representation of knowledge and power of approximate reasoning. Nevertheless, the current representation methods with FPNs are unable to handle the uncertain linguistic knowledge given by domain experts and the reliability of their judgments. In addition, the existing reasoning algorithms have no way to capture the interrelationship of the propositions with the same output transition. Therefore, we present a new type of FPNs, called 2-dimensional uncertain linguistic Petri nets (2DULPNs). The 2-dimensional uncertain linguistic variables (2DULVs) and Choquet integral are combined for knowledge representation and reasoning for the first time. The truth degrees of propositions, thresholds and certainty values of linguistic production rules are denoted as 2DULVs. Some new aggregated operators based on Choquet integral are proposed and used in the approximate reasoning to capture the interactions among antecedent propositions. Finally, an equipment fault diagnosis example is provided to illustrate the correctness and effectiveness of the proposed 2DULPN model. [ABSTRACT FROM AUTHOR]

Each time more companies seek to differentiate themselves from their competitors. For this, they resort to company certification through standards such as ISO 9001 or EFQM. The quality norms permit business to improve process management and production and reduce the lack of knowledge. Therefore, the main objective of this research is to propose a framework to support the process of improving customer's satisfaction according to ISO 9001. To achieve the main objective, the methodology of the design science research process has been followed. First, the problems in the evaluation of customer's satisfaction required by ISO 9001 have been analysed. Second, an expert system has been developed to evaluate the process. To end, the system has been validated. The results suggest that this system facilitates continuous improvement in organizations, providing the customer's vision of the organization, and making possible the implementation of actions oriented to retain customers. This study presents important managerial implications. Insights from this research allow us to offer recommendations that can help practitioners and managers interested in the implementation of expert systems, to focus in quality processes that can increase the chances of a successful adoption.

The ultimate goal of military intelligence is to equip the command and control (C2) system with the decision-making art of excellent human commanders and to be more agile and stable than human beings. Intelligent commander Alpha C2 solves the dynamic decision-making problem in the complex scenarios of air defense operations using a deep reinforcement learning framework. Unlike traditional C2 systems that rely on expert rules and decision-making models, Alpha C2 interacts with digital battlefields close to the real world and generates learning data. By integrating the states of multiple parties as input, a gated recurrent unit network is used to introduce historical information, and an attention mechanism selects the object of action, making the output decision more reliable. Without learning human combat experience, the neural network is trained in fixed- and random-strategy scenarios based on a proximal policy optimization algorithm. Finally, 1,000 rounds of offline confrontation were conducted on a digital battlefield, whose results show that the generalization ability of Alpha C2 trained using a random strategy is better, and that it can defeat an opponent with a higher winning rate than an Expert C2 system (72% vs 21%). The use of resources is more reasonable than Expert C2, reflecting the flexible and changeable art of command.

The article presents an expert system supporting the design of ship's power subsystems, in particular the thruster subsystem. The proposed hybrid expert system uses the results of simulation tests as the additional source of knowledge. The results of system operation are collated in a report which can be used as part of ship design description. The work oriented on developing the expert system is the continuation of the research carried out in cooperation with the shipyard's design office, the main aim of which was to automate selected stages of the ship's design process. The hybrid expert system for computer-aided design of ship thruster subsystems can support designers by creating part of the technical description of the thruster subsystem, evaluation of static and dynamic properties, and by checking if design solutions have met the requirements of classification societies. Additionally, the expert system supports collecting and providing information about the elements and structures of the thruster subsystem. Finally, the system provides a document with the description of the thruster structure and elements used in it. The proposed expert system is dedicated to the initial design stages.

As the complexity of a system increases, the use of sensor networks becomes more frequent and the network health management becomes more and more important. When sensor networks are applied to complex environments, they are influenced by the disturbance factors in engineering practice and observation data may be lost. This will decrease the accuracy of the health state assessment. Moreover, due to the disturbance factors and complexity of the system, observation data and system information cannot be adequately gathered. To deal with the above problems, a new health assessment model is developed based on belief rule base (BRB). The BRB model is one of the expert systems in which the quantitative data and qualitative knowledge can be aggregated simultaneously. In the new health assessment model for a sensor network, a new missing data compensation model based on BRB is constructed first, in which the historical data of the monitoring indicators are used. In addition, the expert knowledge for the historical working state of the sensor network is also applied in the constructed missing data compensation model. Then, based on the compensated data and the observation data of the sensor network, the health state can be estimated by the developed health assessment model based on BRB. Given the uncertainty of expert knowledge, the initial health assessment model cannot assess the health state of the sensor network in an actual working environment. Thus, in this paper, an optimization model is constructed based on the projection covariance matrix adaption evolution strategy (P-CMA-ES). To illustrate the effectiveness of the new proposed model, a practical case study of a sensor network in a laboratory environment is conducted.

The belief rule-base (BRB) model is a new intelligent expert system with the characteristics of both expert system and data-driven model. In BRB there are many if-then rules which use belief degrees to express various types of uncertain information, including fuzziness, randomness, and ignorance. As a semi-quantitative modeling tool for complex systems, BRB has the superiorities of dealing both numerical quantitative data and linguistic qualitative knowledge that are derived from heterogeneous sources. Moreover, it is also a white box approach which can provide direct access and transparency to decision makers and stakeholders. Currently, BRB has been widely applied in many fields, such as decision making, reliability evaluation, network security situation awareness, fault diagnosis, and so on. To fully demonstrate the progress of BRB, the original BRB, and some evolution forms are introduced in this article. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid proposal of a specialist system with a multicriteria decision analysis method, aiming to subsidize decisions in the early diagnosis of psychological disorders. Such disorders can even cause the inability of professionals from various fields of activity, notably of those related to innovation and automation of critical business processes, which exert more pressure on results. These cited disorders cause harm to the professional, to his family, to the company for which he works, to the productive system, and to the social security of a nation. The lack of early diagnosis and the undue attention given to the symptoms only provide reactive and late measures, when the losses have already occurred, in addition to the fact that the professional shows signs of incapacity for work and social life. This paper presents a model that facilitates the process of early diagnosis of various psychological disorders from the qualitative and comparative analysis of events and criteria, using multicriteria methodology associated with a specialist system. Therefore, the proposed model constitutes a modern and consistent tool that contributes to the decision to indicate diagnoses in psychological disorders. Among the various psychological disorders described and categorized in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders of the American Psychiatric Association, this paper highlights the following: schizophrenia spectrum disorders, bipolar disorder, depressive disorders, anxiety disorders, obsessive-compulsive disorder, trauma-related disorders and stressors, dissociative disorders, and disorders related to substances and adverse disorders.

Gestational diabetes mellitus (GDM) is a public health problem. Along with changes in eating habits, increased purchasing power, and climate change, among others, the number of women with gestational diabetes complicated by pregnancy is increasing. GDM generates problems for the mother and for the baby. Therefore, early diagnosis is important to indicate adequate medical follow-up and treatment in a timely manner. In this context, we present a hybrid methodology of a specialized system structured in the Bayesian networks, the multicriteria approach of decision support, and artificial intelligence. In such a methodology, input parameters are proposed in order to support the early diagnosis of GDM, based on the symptoms of diseases that manifest in concomitance or that develop due to the favorable environment caused by the evolution of undiagnosed diabetes. The diseases and symptoms studied were extracted from the medical literature. The diseases were weighted using the Bayesian networks, based on data from the Health Maintenance Organization with coverage in 11 Brazilian states. The weights of the symptoms were tabulated according to the analysis of medical specialists, organized by the multicriteria methodology, applying multiattribute utility theory (MAUT) methods, in particular, MACBETH, by using the Hiview computational tool. Finally, the information was structured in the knowledge base of a specialist system, made in Expert SINTA software.

The fault prediction of the electromagnetic launch (EML) system is an important guarantee to improve the reliability of the system, but there is no mature method that can be directly applied. Combined with the engineering practice of large-scale EML system, a fault prediction method based on knowledge prediction time series is proposed. First, the high-frequency waveform collected in each launch is extended into a time series along the number of launches; second, an intelligent waveform features extraction expert system is constructed to realize feature extraction; third, multidimensional feature sequence prediction and waveform prediction are realized by using two neural networks, respectively; finally, fault prediction is realized by associating the fault diagnosis knowledge. The temperature rising test data of a railgun system for 15 consecutive launches and the recoil stroke test data of noncontinuous 78 launches are used as the input source of the proposed algorithm. The results show that the proposed algorithm can automatically extract the features with fault trend. The single step prediction error of features is less than 1.47%, and the mean square error of curve prediction is half of the results of SARIMA prediction algorithm. Through the temperature rise curves, the proposed algorithm can predict the fault free of the 14th and 15th launch. According to the recoil stroke curves, the fault of the launcher of the 75th launch is predicted. The actual analysis shows that the fault prediction accuracy is high, and the algorithm can significantly improve the system reliability after being applied to engineering. [ABSTRACT FROM AUTHOR]

In previous studies, fuzzy Petri nets (FPNs) have been used for knowledge representation and reasoning in various areas. However, the traditional FPNs have limited abilities in representing uncertain knowledge and conducting approximate reasoning when applied in practical situations. In addition, the knowledge parameters in existing FPNs are usually given by a small number of experts. To address these issues, a grey reasoning Petri net (GRPN) model is proposed in this article for knowledge representation and reasoning under large group environment. In this model, grey production rules in an expert system are modeled by the GRPNs, where grey numbers are used to represent the truth degrees of places, certainty values, and the thresholds on output arcs of transitions. The grey weighted Bonferroni mean operator is adopted as a substitute of the classical min and max operators in the developed grey reasoning algorithm to capture the interrelationships of input places and the interrelationships between transitions. Furthermore, a large group decision-making method is introduced for obtaining the knowledge parameters of GRPNs based on the grey correlation analysis. Finally, the usefulness and effectiveness of the proposed GRPN model is demonstrated by a real-world risk evaluation example. [ABSTRACT FROM AUTHOR]

Intensive attention on personalised skin-health solutions is on account of incomparable love of skin and an urgent need for effective treatment. In the meanwhile, people have great expectations on how to utilise genetic knowledge of our body to provide a precise solution for different individuals, such as daily use of skin-health products, since the rapid development of genetic test services and skin-health science. However, the complexity of multi-modal data, the establishment of correlations between consumer genetic data and product ingredients are the main obstacles encountered today. Determining to settle such obstacles, a personalised recommendation expert system for selecting optimised skin-health product within the category based upon genetic phenotypes for each consumer was introduced in this article. Random Forests were implemented to achieve automatic product categorisation, the performance discussed and compared with SVM and Logistic Regression. Lastly, categorised skin-health product suggestion was made with an optimised recommendation model based on associated genetic phenotype information. Potential changes (up to 71.0% more phenotypic relevant ingredients) from experiments using real product data were demonstrated and compared with imitated cases of real-life human selections. [ABSTRACT FROM AUTHOR]

Determining a consensus opinion on a product sold online is no longer easy, because assessments have become more and more numerous on the Internet. To address this problem, researchers have used various approaches, such as looking for feelings expressed in the documents and exploring the appearance and syntax of reviews. Aspect-based evaluation is the most important aspect of opinion mining, and researchers are becoming more interested in product aspect extraction; however, more complex algorithms are needed to address this issue precisely with large data sets. This paper introduces a method to extract and summarize product aspects and corresponding opinions from a large number of product reviews in a specific domain. We maximize the accuracy and usefulness of the review summaries by leveraging knowledge about product aspect extraction and providing both an appropriate level of detail and rich representation capabilities. The results show that the proposed system achieves F1-scores of 0.714 for camera reviews and 0.774 for laptop reviews.

There are many difficulties and challenges involved in cars failure and malfunction diagnosis. The diagnosis process involves heuristic and complex series of activities and requires specific skills and expertise. A basic toolkit and assistance software are imperatives to help the car drivers to at least identify the source of car failure or malfunction, especially, when the location of the event does not permit immediate help. It enables the car driver to take an initiative in knowing the car condition and try to repeat the car. Expert systems are widely used to embody the diagnosis expertise into machines. However, improving the expert systems' inferencing capability and diagnosis accuracy are still open research topics. Consequently, this paper proposes an agent-based inference engine for the car failure diagnosis expert system that is named automated car failure diagnosis assistance (ACFDA). The agents' goal is to maximize the efficiency of the overall performance of the ACFDA system by deliberating a number of inferencing tasks and tuning the inferencing logical flow. Additionally, the agents' collective effort provides reliable solutions that best fit the users' inputs. The ACFDA system is experimentally tested by 15 relevant candidates. The test results show that the system efficiently and reliably performs the diagnosis to the most given car failure cases. The system can be integrated into cars or can be used as a separate gadget to assist the car drivers in car failure diagnosis and repair.

Accurate detection of characteristic electrocardiogram (ECG) waves is necessary for ECG analysis and interpretation. In this paper, we distinguish four processing steps of detection algorithms: noise and artifacts reduction, transformations, fiducial marks selection of wave candidates, and decision rule. Processing steps combinations from several detection algorithms are used to find QRS, P, and T wave peaks. In addition, we consider the search window parameter modification based on waveform templates extracted by heart cycles clustering. The methods are extensively evaluated on two public ECG databases containing QRS, P, and T wave peaks annotations. We found that the combination of morphological mathematical filtering with Elgendi's algorithm works best for QRS detection on MIT-BIH Arrhythmia Database (detection error rate (DER = 0.48%, Lead I). The combination of modified Martinez's PT and wavelet transform (WT) methods gave the best results for P wave peaks detection on both databases, when both leads are considered (MIT-BIH arrhythmia database: DER = 32.13%, Lead I, DER = 42.52%, Lead II; QT Database: DER = 21.23%, Lead I, DER = 26.80%, Lead II). Waveform templates in combination with Martinez's WT obtained the best results for T wave peaks detection on QT database (DER = 25.15%, Lead II). This paper demonstrates that combining some of the best proposed methods in literature leads to improvements over the original methods for ECG waves detection while maintaining satisfactory computation times.

This paper presents a new approach for determining the impact of lightning strike currents on transmission network elements failures, based on fuzzy logic (FL) and expert systems. The location of lightning strike is determined by means of lightning location system (LLS) and failures in the transmission network, sorted by type of the equipment, are obtained from supervisory control and data acquisition (SCADA) system. The input data set includes two sets. The first set consists of lightning strike locations and current values between the cloud and the ground. The second set consists of current values from SCADA system before and after the fault, protection tripping information, and the state and position of the switches. The proposed FL-based solution is based on a fuzzy decision-making system (DMS), including both data sets in order to provide a power system operator (PSO) with a precise and accurate decision needed in time of emergency. The described model has been tested for functionality and correct results have been obtained, which confirms the membership function (MF) assessment and proves the efficiency and authenticity of the proposed DMS. [ABSTRACT FROM AUTHOR]

In the coffee industry, “cupping” is the process of sensorial evaluation of coffee beans, also known as sample evaluation. This process is done for three major reasons: 1) to determine the actual sensory differences between coffee samples; 2) to describe the flavors of the samples; and 3) to determine preference of product. In totality, cupping targets the measurement of the coffee's quality, which is expressed with a final numerical score. When cupping, the expert judge writes down the individual components’ scores and ranks their intensities for reference. Fuzzy logic has been employed for sensory evaluation of chhana podo (a baked dairy product), also for mango pulp and litchi juice. Moreover, a similar work exists only to train the Honduran Coffee Cuppers. This paper introduces a fuzzy expert system, AI Cupper offering an intuitive way for representing the judge's knowledge by linguistically modeling his perception of the coffee attributes through sensorial evaluation. It is capable of training cuppers when evaluating coffees from several countries and even has the capacity to learn as cupping data flows through it. The system was tested and has shown more than 95% of matching results compared with the experts’ results. [ABSTRACT FROM AUTHOR]

Fuzzy Petri nets (FPNs) are a vital modeling technique for the construction of knowledge-based systems, which have been commonly used in many fields, such as fault diagnosis, risk assessment, workflow management, and disassembly process planning. However, the conventional FPNs have been blamed for the following reasons: 1) the representation parameters in FPNs cannot precisely model experts' experience since it is difficult to manage the fuzziness and randomness of knowledge assessments simultaneously, and 2) the weight coefficients in the existing approximate reasoning algorithms are hardly enough to reflect the associated weights of reordered places. In response, we propose a new type of FPNs, called cloud reasoning Petri nets (CRPNs) based on the concept of interval clouds and the hybrid averaging operator. The cloud production rules in a knowledge-based system are modeled by CRPNs, where the truth degrees of places, the certainty factors of rules, and the thresholds of transitions are represented by interval clouds. Moreover, a matrix operation-based reasoning algorithm is proposed to improve the efficiency of calculating final truth degrees, in which both local and ordered weight coefficients are taken into consideration. Finally, a practical example concerning a power system is provided to demonstrate the usefulness and advantages of the proposed CRPN model. [ABSTRACT FROM AUTHOR]

The rise of personalized diets is due to the emergence of nutrigenetics and genetic tests services. However, the recommendation system is far from mature to provide personalized food suggestion to consumers for daily usage. The main barrier of connecting genetic information to personalized diets is the complexity of data and the scalability of the applied systems. Aiming to cross such barriers and provide direct applications, a personalized expert recommendation system for optimized nutrition is introduced in this paper, which performs direct to consumer personalized grocery product filtering and recommendation. Deep learning neural network model is applied to achieve automatic product categorization. The ability of scaling with unknown new data is achieved through the generalized representation of word embedding. Furthermore, the categorized products are filtered with a model based on individual genetic data with associated phenotypic information and a case study with databases from three different sources is carried out to confirm the system. [ABSTRACT FROM PUBLISHER]

Artificial intelligence (AI) techniques, such as expert systems (ESs), fuzzy logic (FL), and artificial neural networks (ANNs or NNWs) have brought an advancing frontier in power electronics and power engineering. These techniques provide powerful tools for design, simulation, control, estimation, fault diagnostics, and fault-tolerant control in modern smart grid (SG) and renewable energy systems (RESs). The AI technology has gone through fast evolution during last several decades, and their applications have increased rapidly in modern industrial systems. This special issue will remain incomplete without some discussion on AI applications in SG and RESs. The paper will discuss some novel application examples of AI in these areas. These applications are automated design of modern wind generation system and its health monitoring in the operating condition, fault pattern identification of an SG subsystem, and control of SG based on real-time simulator. The concepts of these application examples can be expanded to formulate many other applications. In the beginning of the paper, the basic features of AI that are relevant to these applications have been briefly reviewed. [ABSTRACT FROM PUBLISHER]