A fuzzy expert database system

Fuzzy concepts always exist in much of human reasoning as well as decision making. This paper presents a fuzzy expert database system which is an integration of a fuzzy expert system building tool called SYSTEM Z-II and a database management system called Rdb/VMS. This system is able to extract fuzzy data and terms stored in a database and used in the fuzzy reasoning in an expert system. It can also retrieve information by fuzzy database-queries which are generated by the expert system automatically. Many expert systems in different domain areas such as decision making can be constructed. Sample applications are described to demonstrate the flexibility and power of this system. The fuzzy query language defined and used in the system can also be used independently as a fuzzy enquiry tool in database applications.     

A semantic fuzzy expert system for a fuzzy balanced scorecard

Balanced scorecard is a widely recognized tool to support decision making in business management. Unfortunately, current balanced scorecard-based systems present two drawbacks: they do not allow to define explicitly the semantics of the underlying knowledge and they are not able to deal with imprecision and vagueness. To overcome these limitations, in this paper we propose a semantic fuzzy expert system which implements a generic framework for the balanced scorecard. In our approach, knowledge about balanced scorecard variables is represented using an OWL ontology, therefore allowing reuse and sharing of the model among different companies. The ontology acts as the basis for the fuzzy expert system, which uses highly interpretable fuzzy IF–THEN rules to infer new knowledge. Results are valuable pieces of information to help managers to improve the achievement of the strategic objectives of the company. A main contribution of this work it that the system is general and can be customized to adapt to different scenarios.     

A fuzzy expert system for noise-induced sleep disturbance

In this paper, an attempt has been made to develop a fuzzy expert system for predicting the effects of sleep disturbance by noise on humans as a function of noise level, age, and duration of its occurrence. The modelling technique is based on the concept of fuzzy logic, which offers a convenient way of representing the relationships between the inputs and outputs of a system in the form of IF-THEN rules. It has been established on the basis of findings of various researchers that the effect of noise on sleep disturbance depends to a large extent on age. The middle-aged people have more probability of sleep disruption than the young people at the same noise levels. However, very little difference is found in sleep disturbance due to noise between young and old people. In addition, the duration of occurrence of noise is an important factor in determining the sleep disturbance over the limited range from few seconds to few minutes. Finally, we have compared our model results with some of the findings of researchers reported in International Journals.     

A fuzzy connectionist expert system for visual pattern classification

In this paper, a connectionist model to integrate knowledge-based techniques into neural network approaches for visual pattern classification is presented. We propose a new structure of connectionist model which has rule-following capability as well as instance-based learning capability. Each node of the proposed network is doubly linked by two types of connections: positive connection and negative connection. Such connectionism provides a methodology to construct the classifier from the rule base and allows the expert knowledge to be utilized for the effective learning. For visual pattern classification, we present the techniques for knowledge representation and utilization using the concepts of fuzzy rules and fuzzy relations. We also discuss in this paper some advantageous characteristics of the model: result explanation capability and rule refinement capability. From the experimental results of the handwritten digit classification, the feasibility of the proposed model is evaluated.     

An ergonomic expert system for risk assessment of work-related musculo-skeletal disorders

A computer-based expert system (SONEX) was developed to identify ergonomic risks for work-related musculoskeletal disorders (WRMSDs) in a wide variety of jobs and provide expert prevention advice. SONEX uses a rule base and 6 knowledge base modules: WRMSD risk factors are grouped into two main knowledge base modules (symptoms, engaged body part) with four supplementary knowledge base modules (work environment, work chair, work tools, organization factors). SONEX uses a menu-based interface and a series of simple questions that lead a user through each of the two main modules. Based on user responses it then recommends other knowledge base modules that are relevant for a detailed analysis of work risks. The SONEX rule base has over 140 questions, the knowledge base includes over 200 risk factors, and around 500 possible answers can be generated. SONEX relates ergonomic shortcomings in the job with worker's subjective symptoms; it predicts possible WRMSDs; and it offers preventive suggestions for ergonomic improvements to the job to prevent WRMSDs. It has been tested in a variety of work places with known ergonomic problems and with known employee WRMSDs by comparing its performance with conventional analytical methods and results show that it accurately predicts possible WRMSD risks and identifies ergonomic shortcomings. The advantages of SONEX are that it is much faster than other ergonomic analysis methods and it can be used by ergonomists and other professionals and also by workers themselves.     

Xrays: A fuzzy expert system for qualitative XRD analysis

This paper describes the development of a fuzzy expert system termed XRAYS for identification of minerals via X-ray diffractograms. The system emulates the well-known (manual) Hanawalt method, thus avoiding the black-box approach of most computer search/match programs. The mineral subfile of the JCPDS Powder Diffraction file is stored in a database, from which the Hanawalt groups are created by the program. The expert system then carries out “manual” search following the steps prescribed for the Hanawalt method. Fuzzy comparisons and fuzzy arithmetic operations are employed in searching for matches. A list of candidate minerals is output in decreasing order of confidence. Graphical comparisons between the unknown pattern and candidate patterns are displayed on the screen to allow the diffractionists to make visual comparison as to the degree of match. Several examples containing from two to six minerals are used for illustration.     

A fuzzy expert system for a service centre of spare parts

This paper presents the design of an approximate reasoning framework for an expert system prototype for a service centre of spare parts, to which customers bring failed items for repair. The design development is fundamentally based upon an analysis of a queuing model associated with the service centre system problem. This queuing model provides a prerequisite insight and the knowledge about such a service system. The building process for the framework is described in a case study utilizing the queuing model, namely, M/M/c repair systems with spares. The objective here is to aid management in determining certain decision policies and the capacities which are critical to them. Within the approximate reasoning framework, the identification and the construction of the basic rules that contain uncertain (vague, ambiguous, fuzzy) linguistic terms are described, as well as the specification of the membership functions that represent the meaning of such linguistic terms. Consistency of rules is studied in accordance with the internal relationships between system variables. Approximate Analogical Reasoning with a tree search is used as the inference engine of the expert system. Approximate reasoning results are compared with analytical results.     

A generalised fuzzy reasoning algorithm for an object-oriented expert system tool

Abstract: A fuzzy reasoning algorithm is presented in this paper. It is based on the concept of a generalised fuzzy production rule that adds new conjunctions such as ADD and REL to the usual AND and OR conjunctions for the linkage of premises in conventional rules. A definition is given for weights and related factors, to express and measure a relation between the premises. A formal representation for fuzzy premises is presented, together with the introduction of a fuzzy match method. Multiple thresholds are used to convert the uncertainty measures of reasoning results into linguistic variables. We have applied the fuzzy reasoning algorithm to a frame-based hybrid expert system tool. An object-oriented approach is used in implementing the system. Some results of the application are presented and discussed.     

A VLSI fuzzy expert system for real-time traffic control in ATMnetworks

Concerns a fuzzy logic-based system which has been purposely designed to achieve real-time traffic control in high-speed networks using the asynchronous transfer mode (ATM) technique. One of the most critical functions is “policing”, which has the task of ensuring that each user source complies with the traffic parameters negotiated in the call setup to avoid network congestion. This function is difficult to implement on account of certain conflicting requirements such as selectivity and responsiveness. This is confirmed by the severe limits affecting the most popular mechanisms proposed so far, based on conventional logic. The capacity to formalize approximate reasoning processes offered by fuzzy logic is exploited to derive rules of behavior for a policer starting from the know-how of an expert. We address two key issues related to the implementation of the fuzzy policer. The first focuses on the possibility of hardware implementation of the mechanism using VLSI technology; we present the design of a VLSI fuzzy processor which exhibits a level of performance of over 3 MFLIPS. The second issue concerns the suitability of applying the fuzzy policer to the policing of several classes of sources to reach high levels of cost effectiveness and scalability     

A fuzzy expert system architecture for capability assessments in skill-based environments

The fast pace at which new technologies and techniques are being developed to improve the design and development of products increases the demand for specialized individual skills in the workforce. As a result of higher demands, candidates with exact required skills to work tasks are usually unavailable. Due to the lack of proper methods to assess personnel capabilities, decision makers are forced to assign resources to tasks based on shallow assessments. To tackle this issue, this research presents a layered expert architecture where subcomponents can be customized to specific industrial settings. A fuzzy logic scheme is described to model personnel capabilities as imprecise parameters, and to consider complete skill sets of resources when evaluating their levels of expertise in a skill. The proposed approach leads to thorough capability assessments, as well as an increased number of capable candidates. A case study is presented to show the implementation of the solution approach.     

A fuzzy expert system approach for real-time monitoring of endemic diseases

In this paper, an architecture of LEPDIAG—a knowledge based system for on-line diagnosis and for monitoring prognosis of leprosy—is presented. The important features of LEPDIAG that have been detailed are a multiple expert environment, a homeostatic expert containing the model of immune reaction, a performance evaluator that can compare the observed signs and symptoms with those predicted by homeostatic expert, and a prognostic expert which optimizes the management schedule for the patients. The entire system is built around a fuzzy expert system building tool FRUIT to deal with the imprecise knowledge.     

A fuzzy classification model for myocardial infarction risk assessment

The use of data mining approaches for analyzing patients trace in different medical databases has become an important research field especially with the evolution of these methods and their contributions in medical decision support. In this paper, we develop a new clinical decision support system (CDSS) to diagnose Coronary Artery Diseases (CAD). According to CAD experts, Angiography is most accurate CAD diagnosis technique. However, it has many aftereffects and is very costly. Existing studies showed that CAD diagnosis requires heterogeneous patients traces from medical history while applying data mining techniques to achieve high accuracy. In this paper, an automatic approach to design CDSS for CAD assessment is proposed. The proposed diagnosis model is based on Random Forest algorithm, C5.0 decision tree algorithm and Fuzzy modeling. It consists of two stages: first, Random Forest algorithm is used to rank the features and a C5.0 decision tree based approach for crisp rule generation is developed. Then, we created the fuzzy inference system. The generation of fuzzy weighted rules is carried out automatically from the previous crisp rules. Moreover, a critical issue about the CDSS is that some values of the features are missing in most cases. A new method to deal with the problem of missing data, which allows evaluating the similarity despite the missing information, was proposed. Finally, experimental results underscore very promising classification accuracy of 90.50% while optimizing training time using UCI (the University of California at Irvine) heart diseases datasets compared to the previously reported results.     

An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry

Closed-loop supply chain management has been identified as an efficient, effective and economical strategy towards environmental sustainable practices in manufacturing companies. Without a formidable closed-loop supply chain to complement green supply chain management, most of the goals will not be achieved. A performance evaluation system is crucial for achieving a successful closed-loop supply chain in the automotive industry. Hence, a suitable expert fuzzy rule-based system for evaluation was developed in this study using Visual Basic.Net. The fuzzy rules and arithmetic used were described. The resulting performance measurement system was evaluated using a case study company from the automotive industry. The study culminated with recommendations and proposal of directions for future studies.     

Development of an expert system for preliminary risk assessment of existing concrete dams

The paper presents an expert system to assist in the field inspection of existing concrete dams within the context of a preliminary risk assessment. The paper describes the engineering knowledge and reasoning required to conduct a deterministic field evaluation of the structural stability of the dam. The symptoms and failure modes identified by the expert system along with the required knowledge and procedures are organized in a structured knowledge tree. The instantiation of the frames and firing of the rules for each consultation traces part of the inference tree contained in the structured knowledge tree. Interaction between nearly decomposable problems are executed with metaknowledge procedures, shared rule groups, and active values. Examples are provided.     

A type-2 fuzzy rule-based expert system model for stock price analysis

In this paper, a type-2 fuzzy rule based expert system is developed for stock price analysis. Interval type-2 fuzzy logic system permits us to model rule uncertainties and every membership value of an element is interval itself. The proposed type-2 fuzzy model applies the technical and fundamental indexes as the input variables. This model is tested on stock price prediction of an automotive manufactory in Asia. Through the intensive experimental tests, the model has successfully forecasted the price variation for stocks from different sectors. The results are very encouraging and can be implemented in a real-time trading system for stock price prediction during the trading period.     

A fuzzy information-based approach for breast cancer risk factors assessment

The aim of this study is to define the risk factors that are effective in Breast Cancer (BC) occurrence, and to construct a supportive model that will promote the cause-and-effect relationships among the factors that are crucial to public health. In this study, we utilize Rule-Based Fuzzy Cognitive Map (RBFCM) approach that can successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause-and-effect relationships among the concepts to model the behavior of any system. In this study, a decision-making system is constructed to evaluate risk factors of BC based on the information from oncologists. To construct causal relationship, the weight matrix of RBFCM is determined with the combination of the experts’ experience, expertise and views. The results of the proposed methodology will allow better understanding into several root causes, with the help of which, oncologists can improve their prevention and protection recommendation. The results showed that Social Class and Late Maternal Age can be seen as important modifiable factors; on the other hand, Benign Breast Disease, Family History and Breast Density can be considered as important factors as non-modifiable risk factors. This study is somehow weighing the interrelations of the BC risk factors and is enabling us to make a sensitivity analysis between the scenario studies and BC risk factors. A soft computing method is used to simulate the changes of a system over time and address “what if” questions to compare between different case studies.     

An expert system for structure damage assessment

An expert system called Sperill-II is introduced for the damage assessment of existing structures using the knowledge of experienced structural engineers. Fuzzy sets and Dempster and Shafer's theory are used in this inexact inference method.     

A fuzzy linguistics model for job related injury risk assessment

This paper represents an ongoing research project to develop an Artificial Intelligence model which will predict the likelihood of employee injury in the work place. Proven ergonomic principles will be incorporated into the knowledge base of an expert system. The goal of the system is to provide assistance in employee placement, reducing task related injury, and improving work place conditions. The expert system will use fuzzy set theory to make decisions about the level of risk associated with a particular individual and/or task. The three components of input required are personal characteristics, work place conditions, and environmental factors.     

Embedded fuzzy expert system for Adaptive Weighted Fair Queueing

This paper introduces an embedded fuzzy expert system for Adaptive Weighted Fair Queueing (AWFQ) located in the network traffic router to update weights for output queues. WFQ algorithm allows differentiated service for traffic classes according to Quality of Service (QoS) requirements. Link sharing and packet scheduling methods are the most critical factors when guaranteeing QoS. There are many different scheduling mechanisms but adequate and adaptive QoS aware scheduling solutions are still in a phase of development due to the rapid growth of multimedia in the Internet. The proposed AWFQ model in this work simplifies the link sharing to two service classes: one for UDP and another for TCP. The implementation of the model is based on adaptive change of weight coefficients that determine the amount of allowed bandwidth for the service class. New weight coefficients are calculated periodically on routers according to developed embedded fuzzy expert system. It is shown through simulations that the AWFQ model is more stable and reacts faster to different traffic states than the traditional WFQ scheduler. The embedded expert system adjusts the weights of AWFQ with two parameters that are based on the share of the UDP and TCP input traffic data rate and the change of the share of the UDP and TCP input data rate.