Rule-based Expert System Research Articles

A Hybrid Failure Diagnosis and Prediction using Natural Language-based Process Map and Rule-based Expert System

Preventive maintenance is required in large scale industries to facilitate highly efficient performance. The efficiency of production can be maximized by preventing the failure of facilities in advance. Typically, regular maintenance is conducted manually in which case, it is hard to prevent repeated failures. Also, since measures to prevent failure depend on proactive problem-solving by the facility expert, they have limitations when the expert is absent, or any error in diagnosis is made by an unskilled expert. In many cases, an alarm system is used to aid manual facility diagnosis and early detection. However, it is not efficient in practice, since it is designed to simply collect information and is activated even with small problems. In this paper, we designed and developed an automated preventive maintenance system using experts’ experience in detecting failure, determining the cause, and predicting future system failure. There are two main functions in order to acquire and analyze domain expertise. First, we proposed the network-based process map that can extract the expert’s knowledge of the written failure report. Secondly, we designed and implemented an incremental learning rule-based expert system with alarm data and failure case. The evaluation results shows that the combination of two main functions works better than another failure diagnosis and prediction frameworks.

Child Maltreatment Assessment by using Belief Rule based Expert System

Child Maltreatment Assessment by using Belief Rule based Expert System

Type-2 Fuzzy Rule-Based Expert System for Diagnosis of Spinal Cord Disorders

The majority of people have experienced pain in their low back or neck in their lives. In this paper a type-2 fuzzy rule based expert system is presented for diagnosing the spinal cord disorders. The interval type-2 fuzzy logic system permits us to handle the high uncertainty of diagnosing the type of disorder and its severity. The spinal cord disorders are studied in five categories using historical data and clinical symptoms of the patients. The main novelty of this paper lies in presentation of the interval type-2 fuzzy hybrid rule-based system, which is a combination of the forward and backward chaining approaches in its inference engine and avoids unnecessary medical questions. Using of parametric operations for fuzzy calculations increases the robustness of the system and the compatibility of the diagnosis with a wide range of physicians’ diagnosis. The outputs of the system are comprised of type of disorder, location and severity as well as the necessity of taking a M.R. Image. A comparison of the performance of the developed system with the expert shows an acceptable accuracy of the system in diagnosing the disorders and determining the necessity of the M.R. Image.

Designing a Rule Based Expert Systems for Contact Lenses Patients

Designing a Rule Based Expert Systems for Contact Lenses Patients

Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology

The field of diagnostic decision support in radiology is undergoing rapid transformation with the availability of large amounts of patient data and the development of new artificial intelligence methods of machine learning such as deep learning. They hold the promise of providing imaging specialists with tools for improving the accuracy and efficiency of diagnosis and treatment. In this article, we will describe the growth of this field for radiology and outline general trends highlighting progress in the field of diagnostic decision support from the early days of rule-based expert systems to cognitive assistants of the modern era.

Arabic Speech Act Recognition Techniques

This article presents rule-based and statistical-based techniques for Arabic speech act recognition. The proposed techniques classify an utterance into Arabic speech act categories based on three criteria: surface features, cue words, and contextual information. A rule-based expert system has been developed in a bootstrapping manner based on the fact that Arabic language syntax is inherently rule-based. Various machine-learning algorithms have been used to detect Arabic speech act categories: Decision Tree, Naïve Bayes, Neural Network, and SVM. We compare the experimental results for both techniques (machine-learning and rule-based expert systems). Using a corpus of 1,500 sentences, the rule-based expert system achieved an accuracy rate of 98.92%, while the Decision Tree, Naïve Bayes, Neural Network, and SVM achieved an accuracy rate of 97.09%, 96.48%, 93.50%, and 93.70%, respectively.

Fuzzy rule-based Fine–Kinney risk assessment approach for rail transportation systems

ABSTRACTRail transportation is one of the most crucial public transportation types for big and crowded cities. In rail transportation systems, stakeholders face serious issues involved in workshops, stations, lines and their environments, and general office buildings. In order to reach an increased awareness and better occupational health and safety (OHS) management, a new risk assessment approach is proposed in this study. This approach includes a combination of Fine–Kinney method and a fuzzy rule-based expert system. It captures nonlinear causal relationships between Fine–Kinney parameters. Since there is a high level of vagueness involved in the OHS risk assessment data, the rule-based expert system is developed for probability (P), exposure (E), and consequence (C) for evaluating risk score. A case study is carried out in a rail transportation system in Istanbul/Turkey, and a comparison with the classical Fine–Kinney method is discussed. Results of the case study reveal risk clusters and corresponding control measures that should be taken into consideration. The study methodologically contributes to risk assessment in the knowledge, while case study in a real rail transportation system offers an insight into public transport industry in safety improvement.

A rule-based expert system applied to moisture durability of building envelopes

The moisture durability of an envelope component such as a wall or roof is difficult to predict. Moisture durability depends on all the construction materials used, as well as the climate, orientation, air tightness, and indoor conditions. Modern building codes require more insulation and tighter construction but provide little guidance about how to ensure these energy-efficient assemblies remain moisture durable. Furthermore, as new products and materials are introduced, builders are increasingly uncertain about the long-term durability of their building envelope designs. Oak Ridge National Laboratory and the US Department of Energy’s Building America Program are applying a rule-based expert system methodology in a web tool to help designers determine whether a given wall design is likely to be moisture durable and provide expert guidance on moisture risk management specific to a wall design and climate. The expert system is populated with knowledge from both expert judgment and probabilistic hygrothermal simulation results.

European Forest Types: toward an automated classification

The outcome of the present study leads to the application of a spatially explicit rule-based expert system (RBES) algorithm aimed at automatically classifying forest areas according to the European Forest Types (EFT) system of nomenclature at pan-European scale level. With the RBES, the EFT system of nomenclature can be now easily implemented for objective, replicable, and automatic classification of field plots for forest inventories or spatial units (pixels or polygons) for thematic mapping. Forest Types classification systems are aimed at stratifying forest habitats. Since 2006, a common scheme for classifying European forests into 14 categories and 78 types (European Forest Types, EFT) exists. This work presents an innovative method and automated classification system that, in an objective and replicable way, can accurately classify a given forest habitat according to the EFT system of nomenclature. A rule-based expert system (RBES) was adopted as a transparent approach after comparison with the well-known Random Forest (RF) classification system. The experiment was carried out based on the information acquired in the field in 2010 ICP level I plots in 17 European countries. The accuracy of the automated classification is evaluated by comparison with an independent classification of the ICP plots into EFT carried out during the BioSoil project field survey. Finally, the RBES automated classifier was tested also for a pixel-based classification of a pan-European distribution map of beech-dominated forests. The RBES successfully classified 94% of the plots, against a 92% obtained with RF. When applied to the mapped domain, the accuracy obtained with the RBES for the beech forest map classification was equal to 95%. The RBES algorithm successfully automatically classified field plots and map pixels on the basis of the EFT system of nomenclature. The EFT system of nomenclature can be now easily and objectively implemented in operative transnational European forest monitoring programs.

Rule Based Expert System for Monitoring Real Time Drug Supply in Hospital Using Radio Frequency Identification Technology

A radio frequency identification (RFID) has obtained increasing attention with the emergence of various applications. This study aims to examine the implementation of rule based expert system supported by RFID technology into a monitoring information system of drug supply in a hospital. This research facilitates in monitoring the real time drug supply by using data sample from the hospital pharmacy. This system able to identify and count the number of drug and provide warning and report in real time. the conclusion is the rule based expert system and RFID technology can facilitate the performance in monitoring the drug supply quickly and precisely.

Application of Improved BP Neural Network in Hybrid Control Model of Lime Quality

Abstract In this paper, a method of combining neural network with expert system is proposed, which can realize the control model of real time feedback and control parameters. The model is based on the parameters of production condition, and the current lime quality is predicted. Through the quality of lime and control parameters, using association rule base of expert system, reasoning that lime control parameter adjustment, timely feedback to the lime production control system, to achieve the purpose of real-time control of the quality of lime. The paper presents application of improved BP neural network in hybrid control model of lime quality.

Design a Fuzzy Rule-based Expert System to Aid Earlier Diagnosis of Gastric Cancer.

Introduction:Screening and health check-up programs are most important sanitary priorities, that should be undertaken to control dangerous diseases such as gastric cancer that affected by different factors. More than 50% of gastric cancer diagnoses are made during the advanced stage. Currently, there is no systematic approach for early diagnosis of gastric cancer.Objective:to develop a fuzzy expert system that can identify gastric cancer risk levels in individuals.Methods:This system was implemented in MATLAB software, Mamdani inference technique applied to simulate reasoning of experts in the field, a total of 67 fuzzy rules extracted as a rule-base based on medical expert’s opinion.Results:50 case scenarios were used to evaluate the system, the information of case reports is given to the system to find risk level of each case report then obtained results were compared with expert’s diagnosis. Results revealed that sensitivity was 92.1% and the specificity was 83.1%.Conclusions:The results show that is possible to develop a system that can identify High risk individuals for gastric cancer. The system can lead to earlier diagnosis, this may facilitate early treatment and reduce gastric cancer mortality rate.

A fuzzy rule-based expert system for diagnosing cystic fibrosis.

BackgroundFinding a valid diagnosis is mostly a prolonged process. Current advances in the sector of artificial intelligence have led to the appearance of expert systems that enrich the experiences and capabilities of doctors for making decisions for their patients.ObjectiveThe objective of this research was developing a fuzzy expert system for diagnosing Cystic Fibrosis (CF).MethodsDefining the risk factors and then, designing the fuzzy expert system for diagnosis of CF were carried out in this cross-sectional study. To evaluate the performance of the proposed system, a dataset that corresponded to 70 patients with respiratory disease who were serially admitted to the CF Clinic in the Pediatric Respiratory Diseases Center, Masih Daneshvari Hospital in Tehran, Iran during August 2016 to January 2017 was considered. Whole procedures of system construction were implemented in a MATLAB environment.ResultsResults showed that the suggested system can be used as a strong diagnostic tool with 93.02% precision, 89.29% specificity, 95.24% sensitivity and 92.86% accuracy for diagnosing CF. There was also a good relationship between the user and the system through the appealing user interface.ConclusionThe system is equipped with information, knowledge, and expertise from certified specialists; hence, as a training tool it can be useful for new physicians. It is worth mentioning that the accomplishment of this project depends on advocacy of decision making in CF diagnosis. Nevertheless, it is expected that the system will reduce the number of false positives and false negatives in unusual cases.

A reduced rule-based expert system by the simplification of logic functions for determining the life expectancy of intensive care patients

Purpose Specialist physicians, specialist nurses and personnel in the intensive care unit (ICU) work to treat patients. Compared with other hospital units, ICUs are full-fledged and provide 24-h services. However, patients are admitted to this unit because of conditions such as severe trauma, organ failure or dysfunction and poisoning. The life expectancy of such patients is considerably low. Design/methodology/approach This study developed a reduced rule-based expert system to determine life expectancy. The purpose of this study is to determine the life expectancy of patients using ten variables. The rules were determined by evaluating 210 = 1,024 results. These rules were minimized using Boolean rules for simplification. Findings Conclusions were drawn by evaluating the minimized cases rather than evaluating 1,024 different cases. ICU patients were evaluated using this method, and the mean success rate was 63.75 per cent. Originality/value No rule-based expert system has been designed to determine the death risks of patients in the ICU.

Perancangan Sistem Monitoring Penyelesaian Berkas Pidana Dengan Metode Rule Based Expert Systems dan Teknologi Radio Frequency Identification

The process of criminal cases settlement is based on the regulation of time limit on trial. It is difficult to see the each stage of process because of the number of case files handled. To know the status of settlement, it needs monitoring system of RFID technology built by design to a model of Rule Based Expert Systems. It facilitates the process of data files so it can produce the right, quick and accurate report. The priority of criminal case files is based on the level of completion difficulty while the tracking involves 3 locations: police, public prosecutor and district court. Rule Based Expert System method works following an expert's knowledge in solving a problem. The design that has been made is capable of being implemented and according to the user’s needs. It helps to quickly generate the right information of criminal case settling time process.

Surface Water Mapping by Deep Learning

Mapping of surface water is useful in a variety of remote sensing applications, such as estimating the availability of water, measuring its change in time, and predicting droughts and floods. Using the imagery acquired by currently active Landsat missions, a surface water map can be generated from any selected region as often as every 8 days. Traditional Landsat water indices require carefully selected threshold values that vary depending on the region being imaged and on the atmospheric conditions. They also suffer from many false positives, arising mainly from snow and ice, and from terrain and cloud shadows being mistaken for water. Systems that produce high-quality water maps usually rely on ancillary data and complex rule-based expert systems to overcome these problems. Here, we instead adopt a data-driven, deep-learning-based approach to surface water mapping. We propose a fully convolutional neural network that is trained to segment water on Landsat imagery. Our proposed model, named DeepWaterMap, learns the characteristics of water bodies from data drawn from across the globe. The trained model separates water from land, snow, ice, clouds, and shadows using only Landsat bands as input. Our code and trained models are publicly available at http://live.ece.utexas.edu/research/deepwatermap/ .

A Belief Rule-Based Expert System for Fault Diagnosis of Marine Diesel Engines

This paper proposes a new belief rule-based (BRB) expert system for fault diagnosis of marine diesel engines. The expert system is the first of its kind that consists of multiple concurrently activated BRB subsystems, in which each subsystem has its distinctive outputs and uses the evidential reasoning approach for inference. This novel modeling approach can be applied to identify fault modes that may co-exist. In essence, the group of BRB subsystems is used to model the nonlinear relationships between the fault features and the fault modes in marine diesel engines. The initial BRB expert system can be established by using expert experience and then optimized by using the data samples accumulated during the operation of marine diesel engines. Due to limitations in knowledge and data collected, ignorance is also considered in some BRB subsystems. The proposed BRB expert system is applied to abnormal wear detection for a kind of marine diesel engine. The performance of the BRB expert system is investigated in comparison with that of artificial neural network (ANN) models, support vector machine (SVM) models, and binary logistic regression model with fivefold cross-validation. The results show that the BRB expert system can be used for fault diagnosis of marine diesel engines in a probabilistic manner, which outperforms the ANN models, SVM models, and the binary logistic regression model in terms of accuracy and stability, and can effectively identify concurrent faults.

Supporting breast cancer decisions using formalized guidelines and experts decision patterns: initial prototype and evaluation.

Transparent decisions and its documentation of breast cancer patients' therapy are getting more important especially since modern therapeutic approaches favor personalized forms of treatment. The medical decisions for a treatment are very complex, because there are rules and different options for each patient. To support the decision process, we analyzed the current decision rules and implemented them in a prototype of a rule-based expert system. Thus, this system shall support the quality assurance regarding transparent documentation of individualized therapeutic decisions. For evaluating the system, we used data from a state tumor center and compared the decisions suggested by our system with expert ones. The system and the expert approach will be compared with each other as well as the differences in the treatment decisions. The first preliminary results show us that the human factor-like must be considered by creating a decision support system. The prototype delivers first results, which are restricted, but the results are promising for further developments.

Why autonomous assets are good for reliability – the impact of ‘operator-related component’ failures on heavy mobile equipment reliability

This study examines the maintenance records for components necessary for the comfort and safety of the operators of heavy mobile equipment. The results show that air conditioners, ladders, driver’s seats and mirrors and other required operator-related components can have a significant impact on an asset’s reliability. Analysis was conducted on 10 years of work orders for five identical 1400HP shovels and three identical 1470HP shovels. The results suggest that removing operator-related components contribute to a 15% decrease in the number of work orders and an 8% increase in reliability. In an autonomous asset these components would not be required. The key to this analysis is a rule-based expert system used to clean more than ten thousand work orders and allocate events to specific sub-systems with associated failure modes. While the mining industry has moved to autonomous haul trucks and drills, there are as yet no autonomous shovels. For manufacturers looking at the business case for these units, the availability of data on the reliability increase from removing the operator-related components will be valuable information.

A disjunctive belief rule-based expert system for bridge risk assessment with dynamic parameter optimization model

Abstract Bridge risk assessment is an important approach to avoiding the safety accidents of bridges and ensuring the safety of the public. This can be done by investigating the relationship between bridge risks and bridge criteria. However, such relationship usually is highly complicated in actual situations. In this regard, many approaches were proposed to model bridge risks in the past decades. Particularly, four alternative approaches including the artificial neural network (ANN), evidential reasoning with learning (ERL), multiple regression analysis (MRA), and adaptive neuro-fuzzy inference system (ANFIS) were deeply analyzed and compared for bridge risk assessment. However, these approaches are restricted by their shortages. Thus, this paper utilizes the disjunctive belief rule-based (DBRB) expert system to model bridge risks, where the DBRB expert system is one type of the belief rule-based (BRB) expert system by considering disjunctive belief rules (DBRs) rather than conjunctive belief rules (CBRs) in a BRB. Furthermore, the dynamic parameter optimization model and improved differential evolution (IDE) algorithm are proposed to train the parameters of the DBRB expert system, where the model is applied to ensure the completeness of a DBRB and the algorithm is used to get the global optimal solution. For justification purpose, two existing parameter optimization models and nine alternative models developed by the ANN, ERL, MRA, and ANFIS are applied to assess bridge structures. Comparison results indicate that the DBRB expert system with the dynamic parameter optimization model is better than those alternative models and existing parameter optimization models.