Rule-based Expert System Research Articles

MEASURING SIMILARITY BETWEEN BIOMEDICAL DATA BY USING FURIA ENSEMBLES RULE-BASED CLASSIFICATION

Similarity measures are essential to solve many pattern recognition problems such as classification, clustering, and retrieval problems. Various distance/similarity measures that is applicable to compare two probability density functions. Data comparison is widely used field in our society nowadays, and it is a very import part. To compare two objects is a common task that people from all walks of life would do. People always want or need to find the similarity between two different objects or the difference between two similar objects. Some different data may share some similarity in some given attribute(s). To compare with two datasets based on attributes by classification algorithms, for the attributes, we need to select them out by rules and the system is known as rule-based reasoning system or expert system which classifies a given test instance into a particular outcome from the learned rules. The test instance carries multiple attributes, which are usually the values of diagnostic tests. In this article, we are proposing a classifier ensemble-based method for comparison of two datasets or one dataset with different features. The ensemble data mining learning methods are applied for rule generation, and a multi-criterion evaluation approach is used for selecting reliable rules over the results of the ensemble methods. The efficacy of the proposed methodology is illustrated via an example of two disease datasets; it is a combined dataset with the same instances and normal attributes but the class in strictly speaking. This article introduces a fuzzy rule-based classification method called FURIA, to get the relationship between two datasets by FURIA rules. And find the similarity between these two datasets.

A Deep Learning Inspired Belief Rule-Based Expert System

Recent technological advancements in the area of the Internet of Things (IoT) and cloud services, enable the generation of large amounts of raw data. However, the accurate prediction by using this data is considered as challenging for machine learning methods. Deep Learning (DL) methods are widely used to process large amounts of data because they need less preprocessing than traditional machine learning methods. Various types of uncertainty associated with large amounts of raw data hinder the prediction accuracy. Belief Rule-Based Expert Systems (BRBES) are widely used to handle uncertain data. However, due to their incapability of integrating associative memory within the inference procedures, they demonstrate poor accuracy of prediction when large amounts of data is considered. Therefore, we propose the integration of an associative memory based DL method within the BRBES inference procedures, allowing to discover accurate data patterns and hence, the improvement of prediction under uncertainty. To demonstrate the applicability of the proposed method, which is named BRB-DL, it has been fine tuned against two datasets, one in the area of air pollution and the other in the area of power generation. The reliability of the proposed BRB-DL method, has also been compared with other DL methods such as Long-Short Term Memory and Deep Neural Network, and BRBES by taking into account of the air quality dataset from Beijing city and the power generation dataset of a combined cycle power plant. BRB-DL outperforms the above-mentioned methods in terms of prediction accuracy. For example, the Mean Square Error value of BRB-DL is 4.12 whereas for Long-Short Term Memory, Deep Neural Network, Fuzzy Deep Neural Network, Adaptive Neuro Fuzzy Inference System and BRBES it is 18.66, 28.49, 17.05, 16.37 and 38.15 for combined cycle power plant respectively, which are significantly higher.

Decision Support System and Integrated Asset Optimization (DSS & IAO)

In order to achieve the target of substations maintenance at minimum cost and high reliability, a comprehensive maintenance management system which will enable the execution of maintenance activities incorporating the various optimization strategies and policies is required. In today’s scenario for achieving effective maintenance number of systems, instrumentation technologies, etc. are in use to achieve the targeted benefits. At the substation level these systems are working independently and maintenance engineer is required to coordinate these systems for achieving the synchronization with the overall substation level. Computerized Maintenance Management System (CMMS) with built-in Decision Support System (DSS) are in use to address this issue. Present CMMS systems do not provide a comprehensive platform to implement DSS along with rule base expert systems for event based maintenance and other abnormalities management. In this paper a new platform DSS&IAO which is a comprehensive, integrated and expandable system for the substation maintenance management is proposed. Right from the preventive maintenance to condition based maintenance coupled with event based to equipment specific rule based expert system have been implemented. DSS&IAO is a platform wherein new optimization algorithms, new device interfaces, etc., can be easily incorporated.

Incorporation of the Method of Ranking the Hazards of Abandoned Mine Entries into a Rule-Based Expert System

The work of quantifying the problems of abandoned mines is the first step towards the rehabilitation of these mines. As the result, in all countries that have many abandoned mines, researchers and different organizations have been making efforts to develop decision-making tools, methods, and techniques for rehabilitation of abandoned mines. This paper describes the work conducted to incorporate the method for ranking the problems of abandoned mine entries into a rule-based expert system. This is done using the web-based expert system platform provided by expert system (ES)-Builder Shell. The ES is tested by applying it to the case study of the problems of abandoned mine entries in the areas of Giyani and Musina, Limpopo Province of South Africa. This paper gives details of the procedure followed in creating the production rules of the ES for ranking problems of abandoned mine entries (ES-RAME), its attributes, and the results of its application to the selected case study. The use of the ES-RAME is found to be important for setting the objectives and priorities of the rehabilitation of abandoned mine entries. In addition, the incorporation of the ranking method into the expert system ensured that the procedure of the tanking method is clearly communicated and preserved as the rules of the ES. The expert system also has the advantages of being consistent in its guidance, and it gives the user an opportunity to go through the ranking process of the system using any possible fictitious information; this gives the user a feel for the ranking process and the data required when using the ES-RAME.

Capacity Management of Hyperscale Data Centers Using Predictive Modelling

Big Data applications have become increasingly popular with the emergence of cloud computing and the explosion of artificial intelligence. The increasing adoption of data-intensive machines and services is driving the need for more power to keep the data centers of the world running. It has become crucial for large IT companies to monitor the energy efficiency of their data-center facilities and to take actions on the optimization of these heavy electricity consumers. This paper proposes a Belief Rule-Based Expert System (BRBES)-based predictive model to predict the Power Usage Effectiveness (PUE) of a data center. The uniqueness of this model consists of the integration of a novel learning mechanism consisting of parameter and structure optimization by using BRBES-based adaptive Differential Evolution (BRBaDE), significantly improving the accuracy of PUE prediction. This model has been evaluated by using real-world data collected from a Facebook data center located in Luleå, Sweden. In addition, to prove the robustness of the predictive model, it has been compared with other machine learning techniques, such as an Artificial Neural Network (ANN) and an Adaptive Neuro Fuzzy Inference System (ANFIS), where it showed a better result. Further, due to the flexibility of the BRBES-based predictive model, it can be used to capture the nonlinear dependencies of many variables of a data center, allowing the prediction of PUE with much accuracy. Consequently, this plays an important role to make data centers more energy-efficient.

12-Lead ECG interpretation by database comparison

BackgroundAutomated ECG interpretation is most often a rule-based expert system, though experts may disagree on the exact ECG criteria. One method to automate ECG analysis while indirectly using varied sets of expert rules is to base the automated interpretation on similar ECGs that already have a physician interpretation. The aim of this study is to develop and test an ECG interpretation algorithm based on such similar ECGs. MethodsThe study database consists of approximately 146,000 sequential 12-lead 10 s ECGs taken over the course of three years from a single hospital. All patient ECGs were included. Computer interpretation was corrected by physicians as part of standard care. The ECG algorithm developed here consisted of an ECG similarity search along with a method for estimating the interpretation from a small set of similar ECGs. A second level of differential diagnosis differentiated ECG categories with substantial similarity, such as LVH and LBBB. Interpretation performance was tested by ROC analysis including sensitivity (SE), specificity (SP), positive predictive value (PPV) and area under the ROC curve (AUC).Results: LBBB was the category with the best ECG interpretation performance with an AUC of 0.981 while RBBB, LAFB and ventricular paced rhythm also had an AUC at 0.95 or above. AUC was 0.9 and above for the ischemic repolarization abnormality, LVH, old anterior MI, and early repolarization categories. All other morphology categories had an AUC over 0.8. ConclusionECG interpretation by analysis of ECG similarity provides adequate ECG interpretation performance on an unselected database using only strategies to weight the interpretation from those similar ECGs. Although this algorithm may not be ready to replace rule-based computer ECG analysis, it may be a useful adjunct recommender.

An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

An important aspect of managing a nuclear reactor is how to design refuellings, and from the 1980s to the present different artificial intelligence (AI) techniques have been applied to this problem. A section of the reactor core resembles a symmetrical grid; long fuel assemblies are inserted there, some of them new, some of them partly spent. Rods of “burnable poisons” dangle above, ready to be inserted into the core, in order to stop the reactor. Traditionally, manual design was made by shuffling positions in the grid heuristically, but AI enabled to automatically generate families of candidate configurations, under safety constraints, as well as in order to optimize combustion, with longer cycles of operation between shutdown periods, thus delaying the end-of-cycle point (except in France, where shutdown is on an annual basis, and Canada, where individual fuel assemblies are replaced, with no need for shutdown for rearranging the entire batch). Rule-based expert systems, the first being FUELCON,1 were succeeded by projects combining neural and rule-based processing (a symbolic-to-neural compilation of rules we did not implement), and later on, genetic algorithms in FUELGEN.2 In the literature, one also comes across the application of fuzzy techniques, tabu search, cellular automata and simulated annealing, as well as particle swarms. Safety regulations require simulating the results using a parameter prediction tool; this is done using either nodal algorithms, or neural processing.

An Evolutionary Bootstrapping Development Approach for a Mental Health Conversational Agent.

Conversational agents are being used to help in the screening, assessment, diagnosis, and treatment of common mental health disorders. In this paper, we propose a bootstrapping approach for the development of a digital mental health conversational agent (i.e., chatbot). We start from a basic rule-based expert system and iteratively move towards a more sophisticated platform composed of specialized chatbots each aiming to assess and pre-diagnose a specific mental health disorder using machine learning and natural language processing techniques. During each iteration, user feedback from psychiatrists and patients are incorporated into the iterative design process. A survival of the fittest approach is also used to assess the continuation or removal of a specialized mental health chatbot in each generational design. We anticipate that our unique and novel approach can be used for the development of conversational mental health agents with the ultimate goal of designing a smart chatbot that delivers evidence-based care and contributes to scaling up services while decreasing the pressure on mental health care providers.

Multilayered rule-based expert system for diagnosing uveitis.

Uveitis is a condition caused by inflammation of the uvea, which is the middle layer of the eye. Uveitis can result in swelling or destruction of the eye tissue, which can lead to visual impairment or blindness [1]. Many diseases, either systemic or localized to the eye, are associated with the symptoms of uveitis. Thus, it is often hard to determine the underlying disease responsible for uveitis, especially when the signs and symptoms are unclear. Additionally, there are few experts on uveitis, especially in poor and developing countries. In this paper, we design and build a rule-based expert system to diagnose uveitis. The main motivation for developing this expert system was to mitigate the lack of human experts by helping general ophthalmologists achieve a correct diagnosis with minimal time and effort. Furthermore, the system can act as a good educational tool for newly graduated doctors, guiding their work with their patients and supporting their diagnostic decisions. The novel multilayer design of the system allows flexibility and ease of scaling to new cases in the future. Many techniques were used to improve the system's diagnostic flexibility and overcome incomplete user input. Tests of the system have yielded promising results.

An Improved Rule-Base Expert System for Diagnosis of Obesity

An Improved Rule-Base Expert System for Diagnosis of Obesity

Rule-base formulation for clips-based work ergonomic assessment

Modern societies are dominated by computer-based work. As a result, people tend to be seated for most of their working life. Prolonged sedentariness is known to significantly increase the risk of developing unwanted conditions. This paper presents the development of a rule-based expert system module using CLIPS that provides ergonomic assessment. The system was validated by evaluating pre-recorded user logs from real-life office environments. The tests showed that the system is able to perform the required basic assessment functionality, thus, the implementation of more complex features to advance its development is viable.

Automatic Rule Generator via FP-Growth for Eye Diseases Diagnosis

The conventional approach in developing a rule-based expert system usually applies a tedious, lengthy and costly knowledge acquisition process. The acquisition process is known as the bottleneck in developing an expert system. Furthermore, manual knowledge acquisition can eventually lead to erroneous in decision-making and function ineffective when designing any expert system. Another dilemma among knowledge engineers are handing conflict of interest or high variance of inter and intrapersonal decisions among domain experts during knowledge elicitation stage. The aim of this research is to improve the acquisition of knowledge level using a data mining technique. This paper investigates the effectiveness of an association rule mining technique in generating new rules for an expert system. In this paper, FP-Growth is the machine learning technique that was used in acquiring rules from the eye disease diagnosis records collected from Sumatera Eye Center (SMEC) Hospital in Pekanbaru, Riau, Indonesia. The developed systems are tested with 17 cases. The ophthalmologists inspected the results from automatic rule generator for eye diseases diagnosis. We found that the introduction of FP-Growth association rules into the eye disease knowledge-based systems, able to produce acceptable and promising eye diagnosing results approximately 88% of average accuracy rate. Based on the test results, we can conclude that Conjunctivitis and Presbyopia disease are the most dominant suffering in Indonesia. In conclusion, FP-growth association rules are very potential and capable of becoming an adequate automatic rules generator, but still has plenty of room for improvement in the context of eye disease diagnosing.

Design and Development of Human-Computer Interaction through Judicial Expert System for Violence against Female

This paper focuses on Human-Computer interaction by designing and implementing for Crimes against Women Judicial Expert System (CAWJES). This CAWJES imitates the role of a lawyer or guidance counselor of NGO to offer a virtual mentor. Lawyer or legal advisers will at all times use this system to determine the crime happened on the women. However, CAWJES designed to change this manual system into computerized system. The CAWJES is rule-based expert system. Development of CAWJES carried out by using expert system development life cycle. This paper focuses on Human-Computer Interaction (HCI) applied in CAWJES.

Rule-based expert system for detection of coffee rust warnings in colombian crops

The coffee rust is a devastating disease that causes large economic losses across the world. The severity of this disease changes over time so the farmers are not fully aware of the economic importance of the rust disease in the coffee crops. From a computational science perspective, several investigations have been proposed to decrease the effects caused by the coffee rust appearance from Expert systems based on machine learning techniques. However, because samples about coffee rust incidence are few, the rules created from machine learning techniques do not contain enough information to consider the diversity of scenarios for detecting coffee rust. This paper proposes an expert system based on rules, where the rules are created considering the expert knowledge of specialists and technical reports about the behavior of the disease during a crop year. As far as we know, this is the first expert system proposed using not only expert knowledge but also technical reports in the coffee rust problem. The Buchanan methodology is used to design the proposed system. Experiment results present an average accuracy of 66,67% to detect a correct warning of coffee rust levels.

Automated Identification of Causal Relationships in Nuclear Power Plant Event Reports

A large number of licensee event reports are available in the nuclear power generation sector. A comprehensive analysis of the reports will provide valuable insights for improving nuclear power plant operation and safety. However, the free-text format of the reports poses great challenges to the analysis of the tens of thousands of reports generated. To address this issue, we propose an automated method for the analysis based on natural language processing techniques. Specifically, the objective is to automatically extract the causal relationships from free-text reports. The proposed method relies on a set of keywords that indicates causal relationships and the rules associated with the keywords for identifying the causal relationships, both of which can be identified based on manual analysis of sampled reports and sentences. The rules are described using the parts of speech of the words in a sentence and the dependencies between these words. The keywords and the rules constitute a rule-based expert system, Causal Relationship Identification (CaRI). The proposed method is applied to the analysis of the abstract section of the reports from the U.S. Nuclear Regulatory Commission Licensee Event Report database. We identified 11 keywords and developed 184 rules. The developed system, CaRI, is tested and the result shows that 86% of the causal relationships in the test data can be captured automatically. Application of the proposed method is foreseen in a number of areas, for instance, in the analysis of performance-shaping factors and in reconstruction of the scenario in an event.

The Development of Diseases Identification System in Paddy Plant Using Image Processing Technique

There are three types of paddy leaf disease that have similar symptoms, making it difficult for farmers to identify them, namely Blast Disease, Brown-Spot Disease, and Narrow Brown-Spot Disease. This paper aims to develop an application to identify paddy leaf disease automatically. Several important aspects of the development of software engineering such as usability, interactivity, and simplicity have been considered. Image processing techniques, namely Blobs analysis and color segmentation are used to get the characteristics of diseased leaf; these characteristics are then used to identify the type of diseases using a rule-based expert system. The results obtained indicate that the developed system recognition capability is considered satisfactory with an accuracy of 94.7%.

A methodology for enhancing the reliability of expert system applications in probabilistic risk assessment

In highly complex industries, capturing and employing expert systems is significantly important to an organization's success considering the advantages of knowledge-based systems. The two most important issues within the expert system applications in risk and reliability analysis are the acquisition of domain experts' professional knowledge and the reasoning and representation of the knowledge that might be expressed. The first issue can be correctly handled by employing a heterogeneous group of experts during the expert knowledge acquisition processes. The members of an expert panel regularly represent different experiences and knowledge. Subsequently, this diversity produces various sorts of information which may be known or unknown, accurate or inaccurate, and complete or incomplete based on its cross-functional and multidisciplinary nature. The second issue, as a promising tool for knowledge reasoning, still suffers from lack of deficiencies such as weight and certainty factor, and are insufficient to accurately represent complex rule-based expert systems. The outputs in current expert system applications in probabilistic risk assessment could not accurately represent the increasingly complex knowledge-based systems. The reason is the lack of certainty and self-assurance of experts when they are expressing their opinions. In this paper, a novel methodology is presented based on the concept of Z-numbers to overcome this issue. A case study in a high-tech process industry is provided in detail to demonstrate the application and feasibility of the proposed methodology.

A rule-based expert system for automatic question classification in mathematics adaptive assessment on indonesian elementary school environment

A rule-based expert system for automatic question classification in mathematics adaptive assessment on indonesian elementary school environment

Linguistic Reasoning Petri Nets Using q-Rung Orthopair Fuzzy Linguistic Sets and Weighted Ordered Weighted Averaging Operators

Fuzzy Petri nets (FPNs) are an important tool for knowledge representation and reasoning in the rule-based expert system. Recently, various fuzzy sets and linguistic models have been introduced into FPNs to improve its ability in handling imprecise, fuzzy, and linguistic information. However, the existing FPN models still have the following two deficiencies: The first one is the incompatibility of the knowledge representation parameters in modeling the membership degrees of linguistic variables and hesitancy of experts when experts provide linguistic evaluations. Another one is that the reasoning operators in existing reasoning algorithm considering the local weights and ordered weights of propositions fail to guarantee the reasoning result satisfies the monotonicity, boundary, and idempotency. In this paper, we propose the q-rung orthopair fuzzy linguistic reasoning Petri nets (q-ROFLRPNs) by using the q-rung orthopair fuzzy linguistic sets (q-ROFLSs) to enhance the capability of conventional FPNs in dealing with fuzzy and linguistic knowledge. We define new closed operational laws of q-ROFLSs by linguistic scale functions (LSFs), which not only guarantee the validity and reliability of reasoning results but also handle different semantic situations of the linguistic term set. In addition, an enhanced reasoning algorithm based on weighted ordered weighted averaging (WOWA) operator is proposed by considering the weights of propositions themselves and their ordered weights, and the monotonicity, boundary, and idempotency of the results are satisfied. At last, a case study on fault diagnosis for metro door system is provided to demonstrate the effectiveness and advantages of the proposed model.

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction, and intervention. Deep learning is a representation learning method that consists of layers that transform data nonlinearly, thus, revealing hierarchical relationships and structures. In this review, we survey deep learning application papers that use structured data, and signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.