Knowledge-based Expert System Research Articles

Knowledge-Based Systems for the Assessment and Management of Bridge Structures: A Review

It is becoming an important social problem to make maintenance and rehabilitation of existing infrastructures such as bridges, buildings, etc. in the world. The kernel of such structure management is to develop a method of safety assessment on items which include remaining life and load carrying capacity. The purpose of this paper is to summarize the finding of up-to-date research articles concerning the application of knowledge-based systems to assessment and management of structures and to illustrate the potential of such systems in the structural engineering. In here, knowledge-based systems include knowledge-based expert systems incorporation with artificial neural networks, fuzzy reasoning and genetic or immune algorithms. Specifically, two modern bridge management systems (BMS’s) are presented in the paper. The first is a BMS to assess the performance and derive optimal strategies for inspection and maintenance of concrete bridge structures using reliability based and knowledge-based systems. The second is the concrete bridge rating expert system (J-BMS BREX) to evaluate the performance of existing bridges by incorporating with artificial neural networks and fuzzy reasoning.

Differential diagnosis of hereditary metabolic diseases using the expert knowledge-based system

Aim. The aim of the study was to create a computer decision support system using expert knowledge for the diagnosis of rare hereditary diseases due to the difficulty of their identification at the pre-laboratory stage.Material and Methods. Descriptions of the clinical picture of lysosomal storage diseases from literature sources were used as the research material. The methods included knowledge extraction, expert assessments, quantization of age intervals, and applied intelligent services to form a knowledge base.Results. The results of the study include the construction of models for a complex assessment of a sign and an integral assessment of a disease, on the basis of which the comparative analysis algorithm is implemented to assess each of the hypotheses put forward by the system. The results of testing the prototype of the created expert system on a control sample of patients with mucopolysaccharidosis showed the efficiency of 90%. Discussion. In the discussion, several diagnostic systems are considered and their distinction from the system, presented in this work, is shown.Conclusion. The results of the development of intelligent system based on knowledge for the diagnosis of lysosomal storage diseases are summarized and the perspectives for its development are highlighted.

Method for Attack Tree Data Transformation and Import Into IT Risk Analysis Expert Systems

Information technology (IT) security risk analysis preventatively helps organizations in identifying their vulnerable systems or internal controls. Some researchers propose expert systems (ES) as the solution for risk analysis automation since risk analysis by human experts is expensive and timely. By design, ES need a knowledge base, which must be up to date and of high quality. Manual creation of databases is also expensive and cannot ensure stable information renewal. These facts make the knowledge base automation process very important. This paper proposes a novel method of converting attack trees to a format usable by expert systems for utilizing the existing attack tree repositories in facilitating information and IT security risk analysis. The method performs attack tree translation into the Java Expert System Shell (JESS) format, by consistently applying ATTop, a software bridging tool that enables automated analysis of attack trees using a model-driven engineering approach, translating attack trees into the eXtensible Markup Language (XML) format, and using the newly developed ATES (attack trees to expert system) program, performing further XML conversion into JESS compatible format. The detailed method description, along with samples of attack tree conversion and results of conversion experiments on a significant number of attack trees, are presented and discussed. The results demonstrate the high method reliability rate and viability of attack trees as a source for the knowledge bases of expert systems used in the IT security risk analysis process.

Prototype Development of an Expert System of Computerized Clinical Guidelines for COVID-19 Diagnosis and Management in Saudi Arabia.

The increasing number of COVID-19 patients has increased health care professionals’ workloads, making the management of dynamic patient information in a timely and comprehensive manner difficult and sometimes impossible. Compounding this problem is a lack of health care professionals and trained medical staff to handle the increased number of patients. Although Saudi Arabia has recently improved the quality of its health services, there is still no suitable intelligent system that can help health practitioners follow the clinical guidelines and automated risk assessment and treatment plan remotely, which would allow for the effective follow-up of patients of COVID-19. The proposed system includes five sub-systems: an information management system, a knowledge-based expert system, adaptive learning, a notification and follow-up system, and a mobile tracker system. This study shows that, to control epidemics, there is a method to overcome the shortage of specialists in the management of infections in Saudi Arabia, both today and in the future. The availability of computerized clinical guidance and an up-to-date knowledge base play a role in Saudi health organizations, which may not have to constantly train their physician staff and may no longer have to rely on international experts, since the expert system can offer clinicians all the information necessary to treat their patients.

Application of Cluster Analysis with Fuzzy Logic Elements for Ground Environment Assessment of Robotic Group

Emergency situations, that cause risks for human life and health, dictate elevated requirements to completeness and accuracy of the presentation of information about current ground environment. Modern robotic systems include sensors, that operate on different physical principles. This causes incrementation of information entering control system. Computing resources and technical capabilities of robotic systems are limited in range and detection probabilities of appearing objects. In case of insufficient performance of the on-board computer system and high uncertainties of ground environment, robotic systems are not able to perform without combining information from robotic group and producing a single view of ground environment. Complex information from a group of robotic systems occurs in real time and a non-deterministic environment.
 To solve the problem of identifying attribute vectors related to a single object, as well as to evaluate the effectiveness of obtained solutions, is possible using known formulas of the theory of statistical hypothesis testing and probability theory only under the normal distribution law with the known mathematical expectation of an attribute vector and a correlation matrix. However, these conditions are usually not met in practice. Problems also arise when methods of nonparametric statistics are used with an unknown law of probability distribution.
 The new method of identifying attribute vectors is proposed, that does not rely on a statistical approach and, therefore, does not require knowledge of the type of distribution law and the values of its parameters. Proposed method is based on the idea of combining cluster analysis and fuzzy logic, and is relatively simple to the basic methods of multidimensional nonparametric statistics.
 The results of modeling information processes are presented. The advantages of proposed method are shown. The comparative values for the number of false recognitions are given. The recommendations are given for constructing fuzzy inference rules when creating an expert system knowledge base.

Aplicativo Mobile para avaliar a acessibilidade de Objetos de Aprendizagem utilizando um Sistema Especialista

This paper presents the proposal to develop a mobile application to evaluate the accessibility of Learning Objects (LOs), applying Artificial Intelligence techniques, more specifically an Expert System (ES). The main objective was the development ofa prototype that will assess the accessibility of LOs in text format, initially in PDF and ePub formats, based on guidelines and recommendations that will be stored in the ES knowledge base. The methodology used in the development of the work will be the dissertation-project. As results, it is expected that the application can allow the registration of different LOs and define the level of accessibility of them.

Rule Based Method in Expert System for Detection Pests and Diseases of Corn

Corn is a multi-functional plant, both for direct consumption and as the main raw material for animal feed and the food industry. The need for corn in Indonesia, which continues to increase, needs to be balanced with an increase in production. So that various government policy strategies are carried out, in an effort to increase productivity. Constraints in the cultivation of corn, namely farmers have limitations in identifying diseases that attack plants, and ways to control pests and diseases. The purpose of this study is to propose the use of technology in agriculture by developing an expert system for the detection of corn pests and diseases, which will provide information and strategies for controlling pests and diseases, in order to reduce losses due to crop failure. The knowledge base in expert system contains a set of rules that use the IF-THEN pattern, and to reason on a rule base using forward cahaining method. Expert system is a tool to detection pests and diseases and how to control them like providing expert assistance. In addition, system also provides explanation facility related to the diagnosis results, according to the symptoms obtained from the user. The accuracy detecting pests and diseases 76.6%.

Analysis and design of expert system for microalgae production to produce biofuel

Microalgae are third-generation (3G) biomass sources to produce biofuel. The microalgae process to produce bio-oil consist of several activities such as cultivation, dewatering, and extraction. The cultivation of microalgae mainly using the Open Raceway Pond (ORP) with paddlewheel operation. The dewatering process using the centrifugation and drum filtration reduced the concentration of microalgae slurry to 20%. Furthermore, hydrothermal liquefaction (HTL) was used to extract to the bio-crude. Dissemination tools are needed to deliver knowledge of the microalgae production from cultivation to bioenergy In this research, the expert system was proposed to help the practitioner to apply the microalgae production. The methods used in this research consist of knowledge acquisition, knowledge representation, and development of the inference engine. The result of this study was the knowledge-based expert system for microalgae production. The expert system could help the practitioner in the preparation process of the microalgae cultivation.

Knowledge based Expert System for Predicting Diabetic Retinopathy using Machine Learning Algorithms

Diabetic retinopathy (DR) is a medical condition that can affect the patient's retina and cause leaks in the blood due to diabetes mellitus. The increase in cases of diabetes limits existing manual testing capability. Today new algorithms are becoming very important for assisted diagnosis. Effective diabetes diagnosis can benefit the victims and reduce the negative harmful effects, including blindness. If not treated in a timely manner, this disorder can cause different symptoms from mild vision problems to total blindness. Early signs of DR are the hemorrhages, hard exudates, and micro-aneurysms (HEM) that occur in the retina. Timely diagnosis of HEM is important for avoiding blindness This paper presents PSO feature selection algorithms with three classifications for the detection of Diabetic retinopathy using python.

Knowledge based Expert System for Predicting Diabetic Retinopathy using Machine Learning Algorithms

Diabetic retinopathy (DR) is a medical condition that can affect the patient's retina and cause leaks in the blood due to diabetes mellitus. The increase in cases of diabetes limits existing manual testing capability. Today new algorithms are becoming very important for assisted diagnosis. Effective diabetes diagnosis can benefit the victims and reduce the negative harmful effects, including blindness. If not treated in a timely manner, this disorder can cause different symptoms from mild vision problems to total blindness. Early signs of DR are the hemorrhages, hard exudates, and micro-aneurysms (HEM) that occur in the retina. Timely diagnosis of HEM is important for avoiding blindness This paper presents PSO feature selection algorithms with three classifications for the detection of Diabetic retinopathy using python.

Expert System for Early Diagnosis of COVID - 19

COVID-19 is an illness caused by a novel coronavirus also known as severe acute respiratory syndrome coronavirus 2, which was first identified in the City of Wuhan, China Since then, it has been declared as a global pandemic by the World Health Or-ganization The late diagnosis of COVID-19 patients makes the fast spreading of the virus across the globe Purpose: Thus, the solution to slow down the spread of this virus would be an expert system that will be able to diagnose COVID-19 patients and produce results instantly This paper discusses how a knowledge-based expert system can help to diagnose or detect COVID – 19 in the early stage itself and get the result immediately without any delay © IJCRR

Building A Natural Disaster Knowledge Base Expert System based on the Distributed and Incremental Crawling Technology

China is one of the countries with the most natural disasters in the world. Natural disasters seriously endanger people’s safety and economic situations. Therefore, it is very necessary to strengthen the prediction and research on the characteristics and the causes of various natural disasters. Based on the Internet distributed and incremental crawling technology, this system crawls the knowledge literatures, disaster news and professional disaster data from domestic and overseas on natural disasters, as well as drawing on the advanced knowledge management concept, the captured data will be displayed in the form of knowledge tree. Integrating the authoritative global typhoon live maps and orthophotos which acquired by UAV to better monitor and predict typhoons; This system determined that the SSM framework is used as the system framework, combined MySQL and Redis database to store data stably and efficiently. In addition, the Echarts component can be used to analyze and visualize the crawled data. Big data analysis techniques such as knowledge graphs and related algorithms for literature analysis, data cleaning to better data store and display. In the end, it will enable the researchers to more easily and efficiently carry out the knowledge sharing, collaborative cooperation and interactive communication, which has the certain research significance and use value.

Development of Advisory Knowledge-Based Expert System to Identify and Mitigate Unintentional Insider Threats in Financial Institutions of Ethiopia

Development of Advisory Knowledge-Based Expert System to Identify and Mitigate Unintentional Insider Threats in Financial Institutions of Ethiopia

Knowledge-based expert system to support the semantic interoperability in smart manufacturing

The manufacturing industry has been challenged to find new technologies solutions to improve their manufacturing systems to meet new customise product while ensuring high quality and low costs. Alongside, a considerable increase in the quantity, complexity and detailed information necessary to make all system work, it has increased the need for semantic interoperability over the entire process. The semantic interoperability is necessary to integrate the heterogenic nature of the information that comes from different sources, which could lead a divergent way of interpreting its meaning, causing misinformation, misleads and errors that increase project costs and development time. The semantic interoperability is necessary to integrate the heterogenic nature of the information that comes from different sources and have its meaning interpreted in a divergent way, causing misinformation, misleads and errors that increase project costs and development time. In this context, this work contributes to the development of a Semantic Interoperable Smart Manufacturing to promote semantic interoperability across the manufacturing system considering information and concepts from multiple domains as well as sharing them across different phases of the manufacturing process. The approach proposed was validates in an experimental case of the engine calibration process of an automotive industry. The case was focused on an integration of a new auxiliary brake system composed by an electrical vacuum pump. The experimental system aids gathering and sharing information across the domains of design definitions, project requirements, and calibration process. The results demonstrated the potential of ontology-based systems to promote interoperability, exchanging information and pointing out the inconsistency within a smart manufacturing system.

Research exploring greenhouse environment control over the last 50 years

Environments do not exist in isolation. Their main components in greenhouse systems are plants. Without consideration of plants, analysis of greenhouse environments and environmental control of greenhouses can be accomplished, although it is not simple to achieve. Initial attempts were undertaken to analyze greenhouse environments and then reproduce them. Ventilation rate effects on plant photosynthesis in a growth chamber were reported in 1966. Computer simulations then became a main subject of research. The first dynamic computer simulation of a greenhouse environment including plants was published in 1971. According to innovations of computer technology, the use of minicomputers and microcomputers spread in many areas. By measuring the net photosynthesis of lettuce plants grown under artificial lighting, air temperature was optimized using a minicomputer with the hill-climbing method. The method was designated as the Speaking Plant Approach to environment control (SPA). After the author developed the first reported environmental control system in Japan, systems using microcomputers spread widely for greenhouse environmental control. Knowledge-based expert systems were tested for plant management. Also, a machine vision system was developed to detect critical moments for watering of muskmelon plants. The first feed-forward control method for greenhouses with a large heat mass was reported. Then space farming was tested in 1996 to assess gravity effects on plants. Energy-saving aspects such as solar sterilization, ground heat storage system, and storage using phase change material (PCM) have been reported. Defects of ordinary solarimeters were reported in 2008 along with an approach to estimate evapotranspiration in a greenhouse without the effect of so-called cosine law. Later, this technique was expanded to estimate photosynthesis of the plant canopy in a greenhouse using newly developed sensor units. Keywords: computer control, evapotranspiration, global and diffused solar radiation, nondestructive and non-contact measurement, photosynthesis, SPA DOI: 10.25165/j.ijabe.20191205.5179 Citation: Takakura T. Research exploring greenhouse environment control over the last 50 years. Int J Agric & Biol Eng, 2019; 12(5): 1–7.

ASSESSMENT OF LANDSLIDE-INDUCED MORPHOLOGY CHANGES USING AN OBJECT-BASED IMAGE ANALYSIS APPROACH: A CASE STUDY OF HÍTARDALUR, ICELAND

Abstract. On July 7, 2018, a large landslide occurred at the eastern slope of the Fagraskógarfjall Mountain in Hítardalur valley in West Iceland. The landslide dammed the river, led to the formation of a lake and, consequently, to a change in the river course. The main focus of this research is to develop a knowledge-based expert system using an object-based image analysis (OBIA) approach for identifying morphology changes caused by the Hítardalur landslide. We use synthetic aperture radar (SAR) and optical remote sensing data, in particular from Sentinel-1/2 for detection of the landslide and its effects on the river system. We extracted and classified the landslide area, the landslide-dammed lake, other lakes and the river course using intensity information from S1 and spectral information from S2 in the object-based framework. Future research will focus on further developing this approach to support mapping and monitoring of the spatio-temporal dynamics of surface morphology in an object-based framework by combining SAR and optical data. The results can reveal details on the applicability of different remote sensing data for the spatio-temporal investigation of landslides, landslide-induced river course changes and lake formation and lead to a better understanding of the impact of large landslides on river systems.

A knowledge-based expert system to assess power plant project cost overrun risks

Preventing cost overruns of such infrastructure projects as power plants is a global project management problem. The existing risk assessment methods/models have limitations to address the complicated nature of these projects, incorporate the probabilistic causal relationships of the risks and probabilistic data for risk assessment, by taking into account the domain experts’ judgments, subjectivity, and uncertainty involved in their judgments in the decision making process. A knowledge-based expert system is presented to address this issue, using a fuzzy canonical model (FCM) that integrates the fuzzy group decision-making approach (FGDMA) and the Canonical model (i.e. a modified Bayesian belief network model). The FCM overcomes: (a) the subjectivity and uncertainty involved in domain experts’ judgment, (b) significantly reduces the time and effort needed for the domain experts in eliciting conditional probabilities of the risks involved in complex risk networks, and (c) reduces the model development tasks, which also reduces the computational load on the model. This approach advances the applications of fuzzy-Bayesian models for cost overrun risks assessment in a complex and uncertain project environment by addressing the major constraints associated with such models. A case study demonstrates and tests the application of the model for cost overrun risk assessment in the construction and commissioning phase of a power plant project, confirming its ability to pinpoint the most critical risks involved ̶ in this case, the complexity of the lifting and rigging heavy equipment, inadequate work inspection and testing plan, inadequate site/soil investigation, unavailability of the resources in the local market, and the contractor's poor planning and scheduling.

Knowledge representation using non-parametric Bayesian networks for tunneling risk analysis

Knowledge capture and reuse are critical in the risk management of tunneling works. Bayesian networks (BNs) are promising for knowledge representation due to their ability to integrate domain knowledge, encode causal relationships, and update models when evidence is available. However, the model development based on classic BNs is challenging when expert opinions are solicited due to the discretization of variables and quantification of large conditional probability tables. This study applies non-parametric BNs, which only require the elicitation of the marginal distribution corresponding to each node and correlation coefficient associated with each edge, to develop a knowledge-based expert system for tunneling risk analysis. In particular, we propose to use the pair-wise Pearson's linear correlations to parameterize the model because the assessment is intuitive and experts in the engineering domain are more familiar and comfortable with this notion. However, when Spearman's rank correlation is given, the method can also be used by modification of the marginals. The method is illustrated with a tunnel case in the Wuhan metro project. The expert knowledge of risk assessment for common failures in shield tunneling is integrated and visualized. The developed model is validated by real documented accidents. Potential applications of the model are also explored, such as decision support for risk-based design.

Perancangan Aplikasi Berbasis Android Untuk Diagnosis Penyakit Ginjal Menggunakan Metode Forward Chaining

The pattern of life that bad makes modern society must give up if it is faced with a kidney disease. Kidney disease can get worse because society did not have the time to do health checks, so kidney disease cannot be treated as early as possible. Therefore, the peniliti want to design the application of expert system for diagnosis of early kidney disease. Expert system on this research method using forward chaining rules where it is made into a form of if-then rules. Expert system knowledge base consists of 53 symptom data, disease data, 75 6 data rules and 5 data treatment. System modeling method used is the Unified Modeling Language (UML). Draft form questions to ask the doctor who made such direct symptoms that occur to users. The question is not shown all but will be pursed the possibility of kidney disease. Android-based application designed so that these applications can be run anywhere and anytime as long as the application is installed in a smartphone with Android OS.

Perspective of Postgraduate Biology Students About Onychomycosis

Mechatronics in modern areas of artificial intelligence, as a discipline, has been around for a long time in textiles industries, applications mechatronics in the design of textile machinery, such as 3-D braiding; weaving and LAN systems for weaving; yarn tension compensation; texturing; spinning: measurement automation and diagnosis, knowledge-based expert systems; automated garment manufacture and assembly; and Apparel manufacture. Indeed, most early workers in which branch of textiles industries which was to become electrical engineering were equally at factures with electronic and mechanical artifacts and combined them in various experiments and products. Mechatronic design in textile Engineering contains a selection of contributions to the advanced search which took place in the..... ReadMore