Construction Of Expert System Research Articles

The application of fuzzy logic techniques to improve decision making in apparel size

Traditional set theory, or crisp set theory, is built on the concept of crisp sets. These are sets for which the membership of an element within a set is defined as either true or false; in or out; 1 or 0. This construction is extremely useful, as mathematics has shown, but it struggles to model concepts of our world that possess vagueness or uncertainty. Therefore, we explore an expansion of set theory to allow an element to be partially within a set, thus constituting what is known as a fuzzy set. This paper introduces the basic concept of fuzzy sets, which includes fuzzy sets and crisp sets, as well as the operations of a fuzzy set and fuzzy classification systems. Fuzzy logic has been utilized to solve numerous textile-related difficulties, one of which was determining the proper clothing size. In this study, we examined fuzzy logic applications in textiles, such as the construction of fuzzy expert systems and fuzzy logic for predicting clothing size. This research demonstrates that when determining the correct size of clothing, the outcome is heavily reliant on fuzzy logic.

Prediction System for Diagnosis and Detection of Coronavirus Disease-2019 (COVID-19): A Fuzzy-Soft Expert System

In early December 2019, a new virus named “2019 novel coronavirus (2019-nCoV)” appeared in Wuhan, China. The disease quickly spread worldwide, resulting in the COVID-19 pandemic. In the current work, we will propose a novel fuzzy soft modal (i.e., fuzzy-soft expert system) for early detection of COVID-19. The main construction of the fuzzy-soft expert system consists of five portions. The exploratory study includes sixty patients (i.e., forty males and twenty females) with symptoms similar to COVID-19 in (Nanjing Chest Hospital, Department of Respiratory, China). The proposed fuzzy-soft expert system depended on five symptoms of COVID-19 (i.e., shortness of breath, sore throat, cough, fever, and age). We will use the algorithm proposed by Kong et al. to detect these patients who may suffer from COVID-19. In this way, the present system is beneficial to help the physician decide if there is any patient who has COVID-19 or not. Finally, we present the comparison between the present system and the fuzzy expert system.

Construction of womenswear matching expert system based on domain knowledge

Clothing matching refers to the coordination of style, color, etc. to achieve a decent and generous effect. With the development of artificial intelligence, increasing research efforts have been dedicated to complementary garment collocation as matching clothes to make a suitable outfit has become a daily headache for many people. However, existing studies neglect rules regarding clothing matching, which are based on the knowledge accumulated in the fashion domain and are implicitly transmitted and ambiguous. Towards this end, this article proposes an expert system based on domain knowledge implemented using the Prolog program to realize rule-guided clothing collocation. The article constructs clothing matching rules from four essential clothing attributes: season, type, style, and color, and uses Prolog syntax for knowledge representation. For the formulated facts and rules, the reasoning machine iteratively matches the user's instructions with the system knowledge to reason out suitable matching suggestions. In this study we built a website as a man-machine interface to facilitate friendly interaction and set season, type, style, and color sub-options to match clothing and satisfy user preferences. The system validation based on standard metrics (precision, recall, F1-measure) achieves results above 81%. The main contribution is that the system could match clothes more accurately according to the likes and requirements of users. After comparing with other research, the system is expected to provide suggestions or references for people to choose style and dressing.

Hybrid Autonomous Driving Guidance Strategy Combining Deep Reinforcement Learning and Expert System

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.

The Rationale and Approach of the Legal Expert System Construction

Based on the interpretation on the evolution of theoretical basis of the construction of legal expert system as the clue, this article mainly analyzes and points out the dilemma faced by symbolism and connectionism as the algorithm on construction of legal expert system. Through defeasible reasoning, a contextual-based legal expert system could be realized. The legal expert system should be constructed with the judge as the center character, in which the personality of the judge could not necessarily be eliminated. As a result, the article provides a model of a legal expert system based on dual processing, including factual argumentation and legal argumentation.

Methodology of expert system building

The object of scientific research is the methodology of construction of expert systems. In this article the main aspects and principles of expert systems. After that, the stages of development of expert systems are considered.

Importance-weighted conditional adversarial network for unsupervised domain adaptation

In the construction of expert and intelligent systems, annotating and curating large datasets is very expensive; hence, there is a need to transfer the knowledge from existing annotated datasets to unlabeled data. However, data that are relevant for a specific application usually differ from publicly available datasets because they are sampled from a different domain. Domain adaptation (DA) has emerged as an efficient technique to compensate for such a domain shift. Recent studies have suggested that deep adversarial networks can achieve promising results for DA problems. However, existing adversarial DA methods assign equal importance to different examples and ignore the effect of difference in source domain samples or noise on adversarial performance. Moreover, most DA methods only focus on reducing the distribution difference, but not to learn a good target domain model. To address these issues, we propose an importance-weighted conditional adversarial (IWCA) network for unsupervised DA. In this study, an importance criterion based on domain similarity and prediction certainty is proposed to assign weights to different samples, which can reduce the harmful effects of difficult-to-transfer samples when reducing their cross-domain class conditional distribution differences. Furthermore, a sample selection criterion derived from the perspective of transfer cross validation is used to progressively select appropriate pseudo-labeled target samples to fine-tune the target model. These two criteria work in an EM-like manner that alternating align class conditional distribution for weighted samples and progressively select certain pseudo-labeled target samples to fine-tune the joint model. In this manner, the network will gradually generate features that approximate the actual conditional distribution of the target domain. The results of extensive experiments conducted on four datasets show that IWCA outperforms several state-of-the-art deep DA methods.

Identification of object on an emitted signal based on matrixes of a bispectrum and indicator characteristics of Fourier spectrum

The article considers the urgent problem of identifying the state of an object in order to improve the quality of management. To identify the state of the object, a new method is proposed for analyzing the characteristics of the spectral power of a signal recorded during monitoring, which is characterized by the application of convergent decision production rules of a classification type. It is proposed to carry out convergence based on the analysis of the bispectrum of the acoustic signal emitted by the object and the correlation matrix of the characteristics of the amplitude spectral function. The first approach allows to investigate autocorrelation functions of a signal. The second approach allows to study structure of the organization of a signal. The following are considered: the mathematical apparatus and algorithms for determining specific correlation matrices and the methodology of the decision rules system. Results of synthesis of convergent decisive rules for diagnostics of statuses of an organism at standard forms of bronchitis are given. It is noted that the convergence of decision rules allows to increase the accuracy of identification of the state of the observed object by an average of 5-6% compared to private decision rules and by 10% compared with the classification by an expert person. A distinctive feature is the analysis of not only spectral characteristics, but also the functional relationship between them, which allows us to identify new properties and take into account the states that characterize the change in the functioning of the internal control system as a reaction to external influence as a whole. The obtained research results can be used in the construction of smart expert systems that are part of facility management systems whose state is characterized by emitted acoustic signals.

Fuzzy and Reflection in the Construction of a Medical Expert System

The fuzzy of symptoms (including visual images), representations and assessments in medicine correspond to the peculiarities of the picture of the world of the patient and the physician taking into account the influence of reflection. The continuum of intermediate characteristics of the signs creates serious difficulty for their assessment by physicians. Experts’ confidence factors not only for linguistic features, but also for visual images can help increase the hypothesis quality in intelligent medical diagnostic systems.

Study on the Modeling Method of Knowledge Base System in Web Environment

A knowledge model for knowledge base system in the Web environment is proposed, which includes problem description layer and knowledge layer. A knowledge base is made up of a set of knowledge models. Knowledge models are used to support knowledge representation and reasoning. Visual knowledge modeling tool and visual knowledge service tool based on Web environment realize the construction of knowledge base system in Web environment. The knowledge base system supports the construction of agricultural expert system. By comparison with CommonKADS, it is proved that this method can improve the efficiency of knowledge acquisition, management and maintenance in Web environment. This method also has the reference significance to the construction of the knowledge base system in the Web environment in other domains.

Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy

Human papillomavirus (HPV) infection is related to frequent cases of cervical cancer and genital condyloma in humans. Up to now, numerous methods have come into existence for the prevention and treatment of this disease. In this context, this paper aims to help predict the susceptibility of the patient to forms treatment using both cryotherapy and immunotherapy. These studies facilitate the choice of medications, which can be painful and embarrassing for patients who have warts on intimate parts. However, the use of intelligent models generates efficient results but does not allow a better interpretation of the results. To solve the problem, we present the method of a fuzzy neural network (FNN). A hybrid model capable of solving complex problems and extracting knowledge from the database will pruned through F-score techniques to perform pattern classification in the treatment of warts, and to produce a specialist system based on if/then rules, according to the experience obtained from the database collected through medical research. Finally, binary pattern-classification tests realized in the FNN and compared with other models commonly used for classification tasks capture results of greater accuracy than the current state of the art for this type of problem (84.32% for immunotherapy, and 88.64% for cryotherapy), and extract fuzzy rules from the problem database. It was found that the hybrid approach based on neural networks and fuzzy systems can be an excellent tool to aid the prediction of cryotherapy and immunotherapy treatments.

Using Resistin, Glucose, Age and BMI and Pruning Fuzzy Neural Network for the Construction of Expert Systems in the Prediction of Breast Cancer

Research on predictions of breast cancer grows in the scientific community, providing data on studies in patient surveys. Predictive models link areas of medicine and artificial intelligence to collect data and improve disease assessments that affect a large part of the population, such as breast cancer. In this work, we used a hybrid artificial intelligence model based on concepts of neural networks and fuzzy systems to assist in the identification of people with breast cancer through fuzzy rules. The hybrid model can manipulate the data collected in medical examinations and identify patterns between healthy people and people with breast cancer with an acceptable level of accuracy. These intelligent techniques allow the creation of expert systems based on logical rules of the IF/THEN type. To demonstrate the feasibility of applying fuzzy neural networks, binary pattern classification tests were performed where the dimensions of the problem are used for a model, and the answers identify whether or not the patient has cancer. In the tests, experiments were replicated with several characteristics collected in the examinations done by medical specialists. The results of the tests, compared to other models commonly used for this purpose in the literature, confirm that the hybrid model has a tremendous predictive capacity in the prediction of people with breast cancer maintaining acceptable levels of accuracy with good ability to act on false positives and false negatives, assisting the scientific milieu with its forecasts with the significant characteristic of interpretability of breast cancer. In addition to coherent predictions, the fuzzy neural network enables the construction of systems in high level programming languages to build support systems for physicians’ actions during the initial stages of treatment of the disease with the fuzzy rules found, allowing the construction of systems that replicate the knowledge of medical specialists, disseminating it to other professionals.

МЕТОДИ ПОПЕРЕДНЬОЇ ОБРОБКИ ТОМОГРАФІЧНИХ ЗОБРАЖЕНЬ ОЧНОГО ДНА

The paper deals with a number of modern methods and algorithms for image processing, namely: Kirsch filter, nonlinear filters Robert and Sobel, Wallace and SUSAN methods, as well as software for application of these methods on the example of analysis of tomographic images of macular changes of the retina. The most informative for image segmentation is the Kirsch filter based method and the Sobel nonlinear filter method. In contrast to the so-called deterministic distortions, which are often described by elemental functional transformations of the input image, models of additive, impulse and multiplicative noise are used to describe the random effects. As a rule, Kirsch, Roberts, Sobel algorithms are used to improve the quality of biomedical image processing. Features of construction of high-performance expert systems and devices of image processing and recognition by creation of neural-like methods and algorithms of parallel-hierarchical transformation, network methods of image pre-processing, development and creation of hardware specialized image computers based on them, which are combined with the eye, are considered. video processing (for example, the use of PCF, Q-conversion, parallel-hierarchical image encoding method, space-time ve matrix model of computing processes, parallel algorithms and structures that they reflect for parallel imaging) is a fairly urgent task. Thus, in contrast to the so-called deterministic distortions, which are often described by elemental functional transformations of the input image, it is advisable to use additive, pulse, and multiplicative noise models to describe the random effects. It is shown that, as a rule, the Kirsch, Roberts, Sobel algorithms are used to improve the quality of biomedical image processing.

GRIMP: A machine‐learning method for improving groups of discriminating species in expert systems for vegetation classification

AbstractAimsExpert systems are increasingly popular tools for supervised classification of large datasets of vegetation‐plot records, but their classification accuracy depends on the selection of proper species and species groups that can effectively discriminate vegetation types. Here, we present a new semi‐automatic machine‐learning method called GRIMP (GRoup IMProvement) to optimize groups of species used for discriminating among vegetation types in expert systems. We test its performance using a large set of vegetation‐plot records.MethodsWe defined discriminating species groups as the groups that are unique to each vegetation type and provide optimal discrimination of this type against other types. The group of discriminating species of each vegetation type considerably overlaps with the group of diagnostic species of this type, but these two groups are not identical because not all diagnostic species have sufficient discriminating power. We developed the GRIMP iterative algorithm, which optimizes the groups of discriminating species to provide the most accurate vegetation classification, using a training set of a priori classified plot records. We tested this method by comparing classification accuracy before and after the GRIMP optimization of species groups using vegetation‐plot records from the Czech Republic a priori classified to 39 phytosociological classes, and three initial sets of candidate discriminating species from different sources.ResultsThe GRIMP algorithm improved the classification accuracy at the class level from 65% correctly classified plots in the test dataset before group optimization to 88% thereafter. The other plots were misclassified or unclassified, but misclassifications were reduced by adding further expert‐based criteria considering dominant growth forms.ConclusionsGRIMP‐optimized groups of discriminating species are very useful for semi‐automatic construction of expert systems for vegetation classification. Such expert systems can be developed from an a priori unsupervised or expert‐based classification of at least some vegetation plots.

АНАЛИЗ СИСТЕМ ИНТЕЛЛЕКТУАЛЬНОГО ДИАГНОСТИРОВАНИЯ ЗАБОЛЕВАНИЯ У ПАЦИЕНТА

The review of architectures of construction of expert systems of the analysis of medical data and clinical texts in natural language is carried out. Applied examples of use are considered and actual architectures in medical diagnostics at the moment are revealed.

Fuzzy neural networks to create an expert system for detecting attacks by SQL Injection

Its constant technological evolution characterizes the contemporary world, and every day the processes, once manual, become computerized. Data are stored in the cyberspace, and as a consequence, one must increase the concern with the security of this environment. Cyber-attacks are represented by a growing worldwide scale and are characterized as one of the significant challenges of the century. This article aims to propose a computational system based on intelligent hybrid models, which through fuzzy rules allows the construction of expert systems in cybernetic data attacks, focusing on the SQL Injection attack. The tests were performed with real bases of SQL Injection attacks on government computers, using fuzzy neural networks. According to the results obtained, the feasibility of constructing a system based on fuzzy rules, with the classification accuracy of cybernetic invasions within the margin of the standard deviation (compared to the state-of-the-art model in solving this type of problem) is real. The model helps countries prepare to protect their data networks and information systems, as well as create opportunities for expert systems to automate the identification of attacks in cyberspace.

Application of the Fuzzy Knowledge Base in the Construction of Expert Systems

Application of the Fuzzy Knowledge Base in the Construction of Expert Systems

Modeling of critical systems implementing negative events using dual Petri nets

This work is devoted to solving the problem of modeling critical systems based on the use of modified Petri nets. The dual (binary) Petri net one of the modifications, which allows us to view inversing events at the same time, solving the problem with the possibility of deadlocks. Construction of schemes using fuzzy logic makes it possible to calculate the values of linguistic variables obtained knowledge. Petri dual network allows you to organize the exclusion of negative events by introducing additional links. In accordance with the rules, it is possible to construct a dual fuzzy Petri net, which involves the use of maximum and minimum transitions or appropriate logical calculations of conjunctions and disjunctions. Transition from classical Petri nets to dual fuzzy nets, realizes fuzzy knowledge of logical deriving that gives the chance in construction of expert systems with fuzzy logic solving a problem of data analysis.

AN APPLICATION CONCEPT OF THE FIBRE-OPTICAL TECHNOLOGY IN GEODETIC MONITORING OF ENGINEERING OBJECTS

Monitoring of structures and other different field objects undoubtedly belongs to the main issues of modern engineering. The use of technologies making it possible to implement structural monitoring makes it possible to build an integrated risk management approach combining instrumental solutions with geoinformation systems. In the studies of engineering structures, there is physical monitoring mainly used for examining the physical state of the object - so-called SHM ("Structural Health Monitoring"). However, very important role is also played by geodetic monitoring systems (GMS). The progress observed in the field of IT and automatics has opened new possibilities of using integrated systems on other, often large-scale objects. Based on the current state-of-the-art, the article presents the concept of integration approaches of physical and geodetic monitoring systems in order to develop useful guidelines for further construction of an expert risk management system.

Automation of the early stages of plating lines design

In this paper we propose the procedure of automated selection of technological schemes for plating lines with specific operating conditions and criteria set by the customers using the theory of expert systems construction.