Rule-based Expert System Research Articles

Planning bioinformatics workflows using an expert system.

Bioinformatic analyses are becoming formidably more complex due to the increasing number of steps required to process the data, as well as the proliferation of methods that can be used in each step. To alleviate this difficulty, pipelines are commonly employed. However, pipelines are typically implemented to automate a specific analysis, and thus are difficult to use for exploratory analyses requiring systematic changes to the software or parameters used. To automate the development of pipelines, we have investigated expert systems. We created the Bioinformatics ExperT SYstem (BETSY) that includes a knowledge base where the capabilities of bioinformatics software is explicitly and formally encoded. BETSY is a backwards-chaining rule-based expert system comprised of a data model that can capture the richness of biological data, and an inference engine that reasons on the knowledge base to produce workflows. Currently, the knowledge base is populated with rules to analyze microarray and next generation sequencing data. We evaluated BETSY and found that it could generate workflows that reproduce and go beyond previously published bioinformatics results. Finally, a meta-investigation of the workflows generated from the knowledge base produced a quantitative measure of the technical burden imposed by each step of bioinformatics analyses, revealing the large number of steps devoted to the pre-processing of data. In sum, an expert system approach can facilitate exploratory bioinformatic analysis by automating the development of workflows, a task that requires significant domain expertise. https://github.com/jefftc/changlab. jeffrey.t.chang@uth.tmc.edu.

Modelling Chemical Reasoning to Predict and Invent Reactions.

The ability to reason beyond established knowledge allows organic chemists to solve synthetic problems and invent novel transformations. Herein, we propose a model that mimics chemical reasoning, and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180 000 randomly selected binary reactions. The data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-)discovering novel transformations (even including transition metal-catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph and because each single reaction prediction is typically achieved in a sub-second time frame, the model can be used as a high-throughput generator of reaction hypotheses for reaction discovery.

Computational Conversion via Translation Rules for Transforming C++ Code Into UPPAAL’s Automata

Formal methods help in quantifying the functional and nonfunctional requirements that are later used in the verification process for safety assurance in real-time systems. System formalism is a crucial step in terms of exploring system’s behavior and listing the non-functional requirements. In the context of real-time systems, the non-functional requirements refer to the verification properties of the system. Formalism in software development life cycle refines every process, starting from the formalization of system’s requirements, analysis of system’s behavior and exploring its properties, implementation of the problem’s solution under consideration, and verification of safety critical properties. Rule-based expert system helps in inferring unknown on the basis of some known input, that is, knowledge and rule set. Knowledge is comprised of something known by an individual called an expert of that domain. It requires an expert skill set in order to model and verify some system in model checkers like UPPAAL. This research contribution has explored a variation of traffic light system’s case study, modeled the system in UPPAAL model checker, and later verified the safety critical properties of the generated system like safety, liveness, fairness, reachability, and deadlock freeness to cross-check the validity of transformation rule set. This research is focused on providing the rule-based expert system for inferring timed automata (input of UPPAAL model checker) on the basis of fact cum input, that is, C++ code. Structural facts are used along with the transformation rule set to get the timed automata that verify safety properties of selected case studies—multiple variations of a traffic light system.

Comparative Study of Intelligent Systems for Management of GIT Cancers

Intelligent Systems contribute in the management of different GIT cancer types. The paper discusses different types of intelligent systems, classified according to the medical task achieved, such as early detection, diagnosis and prognosis. It is found out that these types include rule-based and case-based expert systems, artificial neural networks, genetic algorithms, machine learning, in addition to data mining techniques and statistical methods. The study focuses on comparing between different techniques and tools used. The comparison results in identifying the benefits of using data mining techniques for the diagnosis task, since it is based on huge amounts of data in order to discover new patterns hence new predisposing factors. It also points out the use of expert systems in the prognosis task, since this task is mainly based on the specialist experience that should be transferred to less- experienced medical professionals. Based on the previous results, it is recommended to develop an Intelligent Tutoring System (ITS) that focuses on the early diagnosis of GIT cancers, since managing the disease depends mainly on proper diagnosis, and also to build an expert system that helps transferring GIT cancers management knowledge to medical doctors in different hospitals.

Model-Based Probabilistic Reasoning for Self-Diagnosis of Telecommunication Networks: Application to a GPON-FTTH Access Network

Carrying out self-diagnosis of telecommunication networks requires an understanding of the phenomenon of fault propagation on these networks. This understanding makes it possible to acquire relevant knowledge in order to automatically solve the problem of reverse fault propagation. Two main types of methods can be used to understand fault propagation in order to guess or approximate as much as possible the root causes of observed alarms. Expert systems formulate laws or rules that best describe the phenomenon. Artificial intelligence methods consider that a phenomenon is understood if it can be reproduced by modeling. We propose in this paper, a generic probabilistic modeling method which facilitates fault propagation modeling on large-scale telecommunication networks. A Bayesian network (BN) model of fault propagation on gigabit-capable passive optical network-fiber to the home (GPON-FTTH) access network is designed according to the generic model. GPON-FTTH network skills are used to build structure and approximatively determine parameters of the BN model so-called expert BN model of the GPON-FTTH network. This BN model is confronted with reality by carrying out self-diagnosis of real malfunctions encountered on a commercial GPON-FTTH network. Obtained self-diagnosis results are very satisfying and we show how and why these results of the probabilistic model are more consistent with the behaviour of the GPON-FTTH network, and more reasonable on a representative sample of diagnosis cases, than a rule-based expert system. With the main goal to improve diagnostic performances of the BN model, we study and apply expectation maximization algorithm in order to automatically fine-tune parameters of the BN model from real data generated by a commercial GPON-FTTH network. We show that the new BN model with optimized parameters reasonably improves self-diagnosis previously carried out by the expert Bayesian network model of the GPON-FTTH access network.

An Expert System to Assist the Diagnosis of Ischemic Heart Disease

Motivations: Ischemic Heart Disease (IHD) is one of the most common causes of the death in many countries [1]. The disease is unpredictable in onset and hence, requires rigorous diagnosis in regards to multiple signs and symptoms such as cardiac pain, breathlessness, sweating, palpitation, nausea vomiting and hypertension [2]. However, these signs and symptoms cannot be measured with full confidence because of the presence of various uncertainties such as vagueness, imprecision, ambiguity, ignorance and incompleteness [3]. Therefore, traditional diagnosis by the physician lacks the reliability in two ways: a) uncertainty issues in reading the signs and symptoms, and b) difficulty to handle multiple signs and symptoms simultaneously as a human agent. Fuzzy expert systems can be considered for IHD diagnosis; however they are not able to address all types of uncertainty, especially ignorance, incompleteness and ignorance in fuzziness [4]. Such uncertainties were addressed by using a Belief Rule Base (BRB) in the literature in assessing clinical asthma suspicion [3]. In this paper, we propose a Belief Rule Based Expert System (BRBES) to assist the physician to handle all kinds of uncertainty that exist with the signs and symptoms of IHD.Method: The proposed expert system uses BRB to develop the knowledge base and Evidential Reasoning (ER) as inference methodology in an integrated system, named as RIMER [5]. RIMER is a relatively new knowledge representation and inferencing scheme that can handle various types of uncertainties such as vagueness, ambiguity, imprecision, ignorance and incompleteness. Therefore, RIMER allows the handling of various types of uncertainty exist with the signs and symptoms of IHD. The architecture of the proposed BRBES is module based while its layer based user friendly interface allows capturing of input data from the signs and symptoms of IHD as well as allows the assessment of the IHD level of a patient. The whole system has been implemented as a desktop application. The backend data management layer has been constructed using Microsoft SQL server to handle the knowledge base. The interface engine has been developed by using C#.NET.Results and Discussion: A data set of 200 patients has been used to generate results from the BRBES. These have been compared with the results generated from Fuzzy based expert system (FBES) as well as with a manual system where a physician gives the assessment of the IHD level. The Area under Curve (AUC) parameter (the larger the better) of Recover Operating Characteristics (ROC) curves have been used to compare the reliability of the BRBES with FBES and the manual system. The AUC of BRBES is found as 0.949 (95% confidence intervals 0.729 - 0.970) while the AUC of Manual System is 0.811 (95% confidence intervals 0.675 - 0.947) and the AUC of FBES is 0.884 (95% confidence intervals 0.693 - 0.956). The reliability of manual approach is less because the physicians is unable to deliver desired accuracy in assessing IHD due to in most of the cases their mindset of diagnosis is Boolean as observed during our research. It can also be observed from the presented data that the AUC of BRBES is greater than that of FBES because the later considers uncertainties due to vagueness, imprecision and ambiguity while earlier in addition to these uncertainties considers uncertainty due to randomness and ignorance. The proposed system is intended for assisting the physicians in health checkup as an assisting monitoring technology [6].

Fuzzy probabilistic expert system for occupational hazard assessment in construction

Considering the extensive growth of the construction industry in developing countries, the trend of occupational accidents in this sector is growing in recent years. In this regard, developing a hazard management process with a proactive vision makes it possible to identify and prioritize risky points in construction sites and apply preventive measures. Hence, in this paper, a fuzzy probabilistic rule-based expert system is developed for occupational hazard assessment. A fuzzy probabilistic system permits us to model uncertainties related to accident databases and the randomness due to environmental, natural, or time changes. Merging randomness into the occupational risk assessment problem in the construction industry enables the authorities to manage hazards proactively and brings about some practical benefits. The proposed fuzzy probabilistic model benefits from a rule base generated based on fuzzy risk-based statistical and data mining analyses of accident database along with a comprehensive literature review and interviews with experts. This model is tested on four major construction case studies. Through an intensive validation process, the model was successfully analyzed and ranked the risks of different types. The results are encouraging and the model can be implemented in different construction projects.

Augmenting teacher-student interaction in digital learning through affective computing

Interactions between teachers and students can be effectively enhanced if teachers can capture the spontaneous nonverbal behaviors (e.g., facial expressions and body language) of their students in real time, thereby effectively improving teaching strategies and the learning effectiveness of students. In this study, we implemented an expression---response analysis system (ERAS) to analyze facial expressions. The ERAS employs a web camera to capture the facial images of students. Their facial expressions are analyzed to assess their attitude toward progressively more difficult course content, and to determine the relationship between their social interactions and learning effectiveness. The ERAS identified 10 facial feature points that form 11 facial action units (AUs). Subsequently, the AUs were classified as positive, neutral, and negative social interactions by applying a rule-based expert system, and cognitive load theory was applied to verify the classifications. The experimental results showed that student with high coding abilities could adapt to the multimedia digital learning content, as evidenced by the comparatively higher expression of neutral and positive social interactions, whereas students with low coding abilities reported a higher frequency of negative social interactions resulting from the increase in cognitive load. Simultaneously, the real time detection of social interactions can provide a basis for diagnosing student learning difficulties and assist teachers in adjusting their teaching strategies.

A Multi-intelligent Agent System for Automatic Construction of Rule-based Expert System

The main general purpose of this research is the automatic construction of rule-based expert system in diagnosis domain based on an expert system tool and a multi-intelligent agent system. The first goal is used an expert system tool (shell) which is called Diagnosis Domain Tool for Rule-based Expert System (DDTRES) [1]. The second goal is used a multi-intelligent agent architecture for knowledge extraction to elicit knowledge from its resources (domain experts, text documents, databases) for automatic construction of a knowledge base. That means, instead of using traditional methods for knowledge base construction, we used automatic way for that job. In order to achieve second objective, the following agents have been used: The Expert Mining Intelligent Agent (EMIA), The Text Mining Intelligent Agent (TMIA) [2], and The Multi-Intelligent Agent for Knowledge Discovery in Database (MIAKDD) [3]. We are aim to produce an effective final knowledge base by cooperation between EMIA, TMIA, and MIAKDD approaches and integrated with the diagnosis domain tool (DDTRES) to produce a complete rule-based expert system in diagnosis domain. We applied the captured rule-based expert system on heart diseases diagnosis, we found system performance is between a good and a very good range.

Analisis Perbandingan Performa Sistem Pakar Zakat menggunakan Algoritma Rule-Based dan Algoritma Rete Berbasis Web

Web-based expert system developed in this research aims to calculate zakat based on Islam rules, and then compare two algorithms as inference machines, i.e. rule-based algorithm and rete algorithm. The advantages of this expert system are in the way of inputting data, brief explanation about end result, and javascript feature in adding the expertise of the system. In comparing two algorithms, performance parameters used are storage usage in RAM, speed in resulting conclusion, and the accuracy of conclusion, that provided by each algorithm. Pursuing a few iterations in rete algorithm has shown generally better performance compared to rule-based algorithm. This is caused by the fact that several conditions are stored in rete algorithm so that it was not needed to recalculate.
 Index Terms—zakat expert system, rule-based expert system, rete algorithm

An Expert System for Differential Diagnosis of Myocardial Infarction

Automated early detection of myocardial infarction (MI) has been long studied for the purpose of saving human lives. In this paper, we propose a rule-based expert system to analyze a 12-lead electrocardiogram (ECG) for various types of MI. This system is developed by mapping clinical definitions of different types of MI and their differential diagnosis into corresponding algorithmic rule sets. Essential preprocessing steps such as baseline correction, removal of ectopic beats, and median filtering are carried out on recorded ECG. Techniques such as multistage polynomial correction and QRS subtraction are exploited to achieve reliable preprocessing. The processed ECG is then delineated using a time-domain differential-based search algorithm recently proposed by the team to obtain the relevant features and measures. These features and measures are further utilized by an if-then rule set to classify the ECG into various groups. The performance of the system when validated on sample MI database exhibited a sensitivity of 95.7% and specificity of 94.6%. Unlike many previous works, this reliable performance is achieved without the use of abstract classifiers or the need of prior training. Being based on medical definitions, the system is also easily comprehensible, modifiable, and compatible with manual diagnosis.

Intuitionistic fuzzy C-regression by using least squares support vector regression

This paper proposes a novel intuitionistic fuzzy c-least squares support vector regression (IFC-LSSVR) with a Sammon mapping clustering algorithm. Sammon mapping effectively reduces the complexity of raw data, while intuitionistic fuzzy sets (IFSs) can effectively tune the membership of data points, and LSSVR improves the conventional fuzzy c-regression model. The proposed clustering algorithm combines the advantages of IFSs, LSSVR and Sammon mapping for solving actual clustering problems. Moreover, IFC-LSSVR with Sammon mapping adopts particle swarm optimization to obtain optimal parameters. Experiments conducted on a web-based adaptive learning environment and a dataset of wheat varieties demonstrate that the proposed algorithm is more efficient than conventional algorithms, such as the k-means (KM) and fuzzy c-means (FCM) clustering algorithms, in standard measurement indexes. This study thus demonstrates that the proposed model is a credible fuzzy clustering algorithm. The novel method contributes not only to the theoretical aspects of fuzzy clustering, but is also widely applicable in data mining, image systems, rule-based expert systems and prediction problems.

Fuzzy Rule based Expert System for Evaluating Defaulter Risk in Banking Sector

Background/Objectives: Banking sector faced many problems like credit assessment, credit worthiness, and credit risk etc. Many techniques are used to solve these problems in artificial intelligence. But these problems are not fully resolved in previous years. Methods/Statistical Analysis: One of the most important problems is credit risk as customer defaulter risk. Recent studies have not discussed about defaulter or non-defaulter customer. This research work has discussed about customer’s defaulter risk as well as credit risk. Findings: A fuzzy expert system has been designed which can categorized customer as defaulter and non- defaulter. The defaulter risk is calculated by considering factors as CS (CIBIL Score), LVR (Loan to Value Ratio), AAL (Already Availed Loan), IRF (Income Ratio Factor). Data of customers are collected from Indian Overseas Bank (IOB) branches. Different defuzzification methods are used to verify the result of customers. It also verified by the expert in banking domain. Further it is also tested on the data of other branches of banks. Application/Improvements: The system can be helpful for the bank employees in decision making process. In addition to this the system can be used for training new employee for loan approval tasks.

Fuzzy c-means clustering algorithm for directional data (FCM4DD)

Cluster analysis is a useful tool used commonly in data analysis. The purpose of cluster analysis is to separate data sets into subsets according to their similarities and dissimilarities. In this paper, the fuzzy c-means algorithm was adapted for directional data. In the literature, several methods have been used for the clustering of directional data. Due to the use of trigonometric functions in these methods, clustering is performed by approximate distances. As opposed to other methods, the FCM4DD uses angular difference as the similarity measure. Therefore, the proposed algorithm is a more consistent clustering algorithm than others. The main benefit of FCM4DD is that the proposed method is effectively a distribution-free approach to clustering for directional data. It can be used for N-dimensional data as well as circular data. In addition to this, the importance of the proposed method is that it would be applicable for decision making process, rule-based expert systems and prediction problems. In this study, some existing clustering algorithms and the FCM4DD algorithm were applied to various artificial and real data, and their results were compared. As a result, these comparisons show the superiority of the FCM4DD algorithm in terms of consistency, accuracy and computational time. Fuzzy clustering algorithms for directional data (FCM4DD and FCD) were compared according to membership values and the FCM4DD algorithm obtained more acceptable results than the FCD algorithm.

Linguistic Reasoning Petri Nets for Knowledge Representation and Reasoning

This paper proposes a linguistic reasoning Petri net (LRPN) model and develops an ordered weighted linguistic reasoning (OWLR) algorithm for knowledge representation and reasoning. Linguistic production rules in the knowledge base of a decision support system are modeled by LRPNs, where the truth degrees of the propositions in the linguistic production rules and the certainty factors of the rules are represented by linguistic 2-tuples. Moreover, both local and global weights of knowledge rules are taken into account in the linguistic reasoning process. The developed OWLR algorithm can allow the rule-based expert systems modeled with LRPNs to execute knowledge reasoning in a more flexible and intelligent manner. Finally, a case study regarding production rescheduling is presented to show the effectiveness and benefits of the proposed LRPN model and the linguistic reasoning approach.

A method for automatic gamelan music composition

Aim of this study is designing a method for automatic gamelan music composition using rule-base expert system approach. The program is designed for non-expert user in order to help them composing gamelan music or analyzing their composition to achieve explanation and recommendation of ideal composition. There are 2 essential components in this method, which are knowledge and inference. Knowledge is represented into basic knowledge and melodic knowledge. Basic knowledge contains rules that control the structure of gamelan song, and melodic knowledge supports system in composing or analyzing notations sequence that fit the characteristics of melody in gamelan music. Basic knowledge represents basic rules of gamelan music that have quantitative value, so deterministic approach is used for basic knowledge acquisition. Melodic knowledge consists of dynamic data, so stochastic approach is used to create the melodic knowledge base. The rules of composing and analyzing a composition are defined based on basic knowledge and melodic knowledge. The inference engine is designed to compose and analyze a composition. Automatic composition for gamelan music is proposed using Generate and Test method (GAT) with random technique, and composition analysis is proposed using backward chaining method

A Novel Approach for Quality of Life Evaluation: Rule-Based Expert System

This article presents a completely novel and innovative approach for quality of life evaluation—the usage of expert systems. The use of an expert system to evaluate the degree of quality of life is a research project, based entirely on a qualitative method, which aims at helping in understanding of how in an era of great changes residents from different countries live and work in diverse economic organizations, and how they would rate their work and life. In this article the architectural framework of an expert system in the social area domain is presented; the design and the development process of the system are also described. The designed system is intended for the evaluation of quality of life. The development of expert system for quality of life evaluation is a new information technology derived from artificial intelligent research. The novel expert system contains knowledge about sets of factors and indicators, which may be used for quality of life measure (equal protection by the law; freedom from discrimination; right to be treated equally without regard to gender, race, language, religion, political beliefs; and etc.) and works with them. The knowledge for the expert system was acquired from domain experts, texts, and other related sources. The tasks of the presented expert system are: to interact, to access knowledge, to make logical decisions, to result in conclusions, and to interpret its conclusions.

Fuzzy Rule-Based Expert System for Determining Trustworthiness of Cloud Service Providers

Cloud Computing has a great potential to transform the way we use technology. People have started using cloud services but they still have a lot of apprehensions resulting from the lack of trust on Cloud Service Provider (CSP) and the associated security risks. In this paper, we have proposed a fuzzy rule-based expert system that evaluates trustworthiness of a CSP from the viewpoint of consumers (called Direct-Trust), their Public-Review, and the Cloud Auditor (called Auditor-Trust). A survey has been conducted to rate the five CSPs on the basis of their Reliability, Performance, Security, and Usability. The data collected from the survey serve as the dataset to calculate the Direct-Trust and Public-Review. Auditor-Trust has been calculated using the data generated by Nasuni, a cloud storage company which evaluates CSPs on the basis of their Performance, Availability, Scalability, and Accuracy. Final Trustworthiness is then calculated by combining Direct-Trust, Public-Review, and Auditor-Trust. All the simulations have been done using Fuzzy Logic Toolbox of MATLAB. The results generated rank the CSPs according to their Final Trustworthiness. A scale of 1–5 has been used in all the calculations for ratings.

Dynamic rule adjustment approach for optimizing belief rule-base expert system

The belief rule-base (BRB) inference methodology, which uses the evidential reasoning (RIMER) approach, has been widely popular in recent years. As an expert-system methodology using the RIMER approach, BRB is used for storing various types of uncertain knowledge in the form of belief structure. Several structure-learning approaches have been proposed in recent years. However, these approaches are deficient in various aspects, do not have repeatability, hold incomplete data, and are constrained by the associated scale-utility value. Moreover, considering the influence of the number of rules for a BRB system, two scenarios are designed to reveal the relationship between structure feature and fewer/excessive rules. Excessive rules may lead to a BRB that is equipped with an over-complete structure, whereas significantly fewer rules may result in a BRB with an incomplete structure. To solve these problems, we initially proposed to develop an adjusted structure that is leading to the establishment of a complete structure instead of incomplete and over-complete structures. By scenario analysis and experimental verification through parameter learning of BRBs, we summarize several features of two scenarios, which can be used to reveal certain number of key BRB properties. Finally, density and error analyses are introduced to dynamically prune or add rules to construct the complete structure, particularly that of the BRB comprising multiple-antecedent attributes. We verify the effectiveness of the proposed approach by testing its use in a practical case study on oil pipeline-leak detection and demonstrate how the approach can be implemented.

An expert system for the quantification of fault rates in construction fall accidents

Introduction. Expert witness reports, prepared with the aim of quantifying fault rates among parties, play an important role in a court's final decision. However, conflicting fault rates assigned by different expert witness boards lead to iterative objections raised by the related parties. This unfavorable situation mainly originates due to the subjectivity of expert judgments and unavailability of objective information about the causes of accidents. As a solution to this shortcoming, an expert system based on a rule-based system was developed for the quantification of fault rates in construction fall accidents. The aim of developing DsSafe is decreasing the subjectivity inherent in expert witness reports. Methodology. Eighty-four inspection reports prepared by the official and authorized inspectors were examined and root causes of construction fall accidents in Turkey were identified. Using this information, an evaluation form was designed and submitted to the experts. Experts were asked to evaluate the importance level of the factors that govern fall accidents and determine the fault rates under different scenarios. Based on expert judgments, a rule-based expert system was developed. The accuracy and reliability of DsSafe were tested with real data as obtained from finalized court cases. Result. DsSafe gives satisfactory results.