Rule-based Expert System Research Articles

Web-based rule-based expert system to screen postpartum depression

The high prevalence of postpartum depression significantly increases maternal morbidity. Lack of diagnosis for postpartum depression results in mothers missing early treatment necessary. The traditional screening method using questionnaire presents its own disadvantage in diagnosis. Therefore, development of postpartum screening method that focuses on ease in implementation is very much needed to improve postpartum maternal health. This paper proposes a screening information system for postpartum depression to help its proper treatment. This research employed the rule-based method to diagnose postpartum depression and the expert system to input symptoms data that will result in diagnoses. Respondents input symptoms data to be diagnosed by the system. The system then automatically generate output of suggested treatment based on expert input associated with the diagnoses the system is working on. This research employed the Randomized Control Trial method with Control Group Pre and Post Tests Design. Samples of this research were 52 postpartum mothers consisting of 26 respondents in the system group, and the other 26 in the manual group. Results show that 42.31% (n=11) of mothers suffer from postpartum depression. There is a significant difference in EPDS score prior to and after the use of the information system (P=0.000). The system also successfully generated very high TAM score, meaning that it is capable of postpartum screening along with ease of use and some other advantages. Therefore, screening for postpartum depression using information system is very effective and ensures proper and timely treatment.

A transparent rule-based expert system using neural network

Classification is one of the foremost machine learning tasks in this modern era. Neural Network (NN) is one of the powerful classification techniques. NN can achieve high classification accuracy on highly imbalanced and complex datasets, but lacks in explanation of its reasoning process which limits its applicability in various domains which require transparent decision along with good accuracy. There are some techniques which extract rules from NN and make it transparent; however, attribute pruning, rule pruning and class overlap algorithms are not sufficiently effective. Therefore, this paper proposes a rule extraction algorithm, called Transparent Rule Extraction using Neural Network (TRENN) to convert NN into white box with greater emphasis on attribute pruning and rule pruning. The proposed TRENN is a pedagogical approach and an extension of one of the existing algorithms named Rule Extraction from Neural Network using Classified and Misclassified data (RxNCM). The proposed TRENN extends the RxNCM with sequential floating backward search for feature and rule selection to improve the comprehensibility of the generated rules. Besides, the proposed TRENN uses probabilistic approach for the treatment of class overlapping problem in the rule updating phase instead of reclassification used in RxNCM where the overlap may persist. Experiments are conducted with eight real datasets collected from the UCI repository. Performance of the TRENN is measured with Precision, Recall, FP-Rate, F-measure, and local and global comprehensibility. It is observed from the experimental results that TRENN performs better than Re-RX, RxNCM and RxREN.

Integrating 3D modeling, sustainability and cost estimating at the conceptual design stage of bridges

Integrated 3D modeling adaptation to infrastructure has been slow due to technical challenges, such as the lack of interoperability between software. To fill this gap, a versatile model with 3D modeling capabilities was developed to assist in designing sustainable bridges at the conceptual design stage. The model incorporates a rule-based expert system and four modules, namely, 3D CAD modeling, bridge sustainability rating system (BrSRS), bridge environmental performance strategy mapping (BrEPSM), and conceptual cost estimation. The 3D CAD module was developed using the Graphics.DrawLine method. The BrSRS and BrEPSM modules were developed by amalgamation of the identified pertinent sustainable and footprint indicators. The model aims to provide bridge type recommendations, allow customization of a sustainable bridge, illustrate forecasted footprint levels, present the conceptual design in AutoCAD’s 3-dimensional (3D) mode, facilitate a recalculation process when the bridge’s dimensions are altered, and generate a conceptual cost estimate.

An Optimal Learning Model for Training Expert System to Detect Uterine Cancer

Abstract In both developed and emerging countries, like Bangladesh, uterine cancer is one of the most common cancers for women. It is also the sixth most common cancer among females in the world and the fourteenth most common cancer in total. In recent years, the high prevalence of uterine cancer in women has risen dramatically. This covers several aspects that are linked to the signs and symptoms of this disease that are to be assessed and tested. Typically, these dimensions are expressed in quantitative and qualitative forms and also exist different kinds of uncertainty. Therefore, by using suitable methods to resolve the problem of uncertainty; otherwise, the detecting procedure will become unreliable. There are several systems proposed in this paper to tackle the problem. None of them, however, is worthy of solving the problem of ambiguity. This paper explains the development of a Belief Rule Based Expert System using the RIMER method, which by taking into account signs and symptoms, is able to detect the involvement of malignant tumors and risk factors for this cancer under uncertainty. Specifically, the system will expect a huge employment in diminishing the cost of lab assessments. The structure will support patients in making judicious strides well early. Moreover, this research has been done on MATLAB environment shown that the yields produced using the made system are more reliable than from manual structure, artificial neural network (ANN) and support vector machine (SVM).

Rule-based expert system prototype to determine the intellectual coefficient in university students

The Wechsler scale of intelligence for adults-III (WAIS-III) is based on 14 subtests. It is used in the Metropolitan University of Ecuador to evaluate the students’ intellectual coefficient (IQ). Each subtest measures a different facet of intelligence, producing the three IQ scores: verbal, performance, and total scales, as well as four index scores: processing speed, perceptual organization, working memory, and verbal comprehension. The development of an expert system (ES) that evaluates the IQ, according to WAIS-III can help in the tabulation of these results, since the process is manual. The KLIC methodology facilitated the creation of the ES and a shell allowed the development of knowledge bases (KB). The ES is composed of fourteen KB, which are responsible for performing the corresponding function of each of the subtests. The rules of production in the KB investigate what the student knows about incomplete figures, vocabulary, cube design, matrices, among other aspects.

Optimization-assisted water supplement mechanism with energy efficiency in IoT based greenhouse

Remarkable resource management and energy efficiency improvements can be achieved in greenhouses using innovative technological advancements and modern agricultural methods. Deployment of Internet of Things (IoT) and optimization algorithms in greenhouse farming is highly desirable for real-time monitoring and controlling various parameters with optimal solutions. However, IoT based greenhouses require more energy as compared to traditional farming. This paper proposes an optimal greenhouse water supplement mechanism with efficient energy consumption based on IoT and optimization techniques. The first contribution of this study is to gather the actual water and soil moisture levels from the greenhouse and tank using IoT devices. Secondly, the formulation and deployment of an objective function to compute the optimal water and soil moisture levels for greenhouse and tank based on user-desired settings, the system constraints and actual sensing values. We applied a rule-based expert system to activate water pumps with the required flow rate and operational duration to achieve efficient energy consumption. To prove the effectiveness of the proposed concept, embedded IoT devices and objective function for optimization are deployed as well as, a number of experiments are conducted to provide the optimal water and soil moisture levels in a real greenhouse and water tank environment.

Automatic malaria disease detection from blood cell images using the variational quantum circuit

Variational quantum circuit (VQC) is a quantum-classical (QC) machine learning approach that accommodates quantum processes on a classical computer. Malaria is a worldwide deadly disease caused by Plasmodium parasites. This research designs an effective VQC-based approach to recognize the existence of malaria from the Red blood cell (RBC) image through the classification of the optimized feature set that has been extracted from a set of RBC images. Minimum redundancy maximum relevance (mRMR), and Principal component analysis (PCA), are used to optimize the feature set. Comparing to existing classical approaches we have found that mRMR with our input encoding and parameterized circuit of VQC shows satisfactory performance by using a lower number of features and a lower number of parameters. After ascertaining the presence of malaria from VQC we have also introduced a rule-based expert system to detect the types of malaria. The proposed mechanism is mainly designed to evaluate the potency of quantum machine learning (QML) in near-term quantum computers and using the ten-fold cross-validation the scheme gained an overall accuracy, precision, recall, and specificity of 99.06%, 99.08%, 99.05%, and 99.07% respectively for malaria disease diagnosis.

Predicting heart failure using data mining with Rough set theory and Fuzzy Petri Net

The Rough Set Theory (RST) is a method that has proven its efficiency and simplicity in machine learning and successfully developing now a day’s vastly and rapidly. Fuzzy Petri nets (FPNs) are a potential modelling technique, which is used for knowledge representation and reasoning of rule-based expert systems. Though, RST has the efficiency in dimension reduction, it has to be proved with evidence by creating model using FPN with rule based reasoning. In this paper the induction of decision rules by RST executed with Fuzzy Petri Nets (FPN) is analyzed in the sense how it performed better than other data mining classifiers. The rule-based classifiers like jrip, part R, zero R are used for the comparison purposes. The knowledge captured from the rules through the best reduct has performed with the efficiency of RST and formulating rules by FPN. This paper experiments the Heart failure data to investigate the decision making from the rules generated by LERS system of RST with the approach of FPN. The heart failures during the follow up period of the patents are predicted with the pattern recognized form the data using the above process and the best evaluators are found during the experiments were pictured.

The rapid development of knowledge bases using UML class diagrams

The knowledge bases design that employs conceptual models and their transformations is one of the most interesting areas of knowledge engineering and it remains critical. This paper presents an approach to the rapid development of knowledge bases for rule-based expert systems on the basis of the model-based generation of program codes. The approach proposed uses conceptual models in the form of UML class diagrams as a source of knowledge. In accordance with the technique that we propose, diagram concepts and relationships are extracted, mapped to the ontology of a subject domain and represented in the form of fact and rules templates. The original notation, namely the rule visual modelling language (RVML), is used to visualise and modify mapped elements. The CLIPS is used as the targeted knowledge base programming language. This approach does not eliminate errors due to conceptual models; but provides minimisation of programming errors resulting from hand coding.

Internet Financial Fraud Detection Based on a Distributed Big Data Approach With Node2vec

The rapid development of information technologies like Internet of Things, Big Data, Artificial Intelligence, Blockchain, etc., has profoundly affected people's consumption behaviors and changed the development model of the financial industry. The financial services on Internet and IoT with new technologies has provided convenience and efficiency for consumers, but new hidden fraud risks are generated also. Fraud, arbitrage, vicious collection, etc., have caused bad effects and huge losses to the development of finance on Internet and IoT. However, as the scale of financial data continues to increase dramatically, it is more and more difficult for existing rule-based expert systems and traditional machine learning model systems to detect financial frauds from large-scale historical data. In the meantime, as the degree of specialization of financial fraud continues to increase, fraudsters can evade fraud detection by frequently changing their fraud methods. In this article, an intelligent and distributed Big Data approach for Internet financial fraud detections is proposed to implement graph embedding algorithm Node2Vec to learn and represent the topological features in the financial network graph into low-dimensional dense vectors, so as to intelligently and efficiently classify and predict the data samples of the large-scale dataset with the deep neural network. The approach is distributedly performed on the clusters of Apache Spark GraphX and Hadoop to process the large dataset in parallel. The groups of experimental results demonstrate that the proposed approach can improve the efficiency of Internet financial fraud detections with better precision rate, recall rate, F1-Score and F2-Score.

Process abnormity identification by fuzzy logic rules and expert estimated thresholds derived certainty factor

Abnormity identification plays a significant role in keeping a process healthy and safe. The rule-based expert system (RB-ES) is an efficient approach in this field. However, the relationship between propositions is fuzzy and uncertain when the process knowledge is summarized into rules. Moreover, the process data utilized as observed evidence to activate the uncertain rules face the imprecise measurement problem. The imprecise measurement problem comes from noises, heavy dust, grease pollution, and other circumstance factors that influence sensors. To address the above issues, an abnormity identification framework based on fuzzy logic rules is proposed in this work. Two kinds of uncertain rules with fuzzy linguistic information and expert estimated threshold in premises are specially considered. First, a deviation estimation method based on probability distribution is proposed to extend the imprecise data into the interval data to solve the uncertain problem caused by imprecise measurement. Second, the fuzzy piecewise membership function is utilized to calculate the certainty factor (CF) of the premise in rules, which describes the occurrence risk of an event or abnormity. Third, the inference steps based on the CF expert system with uncertain rules and imprecise activated variables are presented to identify the abnormity in the complex industrial processes. The proposed method is applied to the thickening process in hydrometallurgy and performs better than the traditional rule-based method and the PLS method on the actual process data. The accuracy of the proposed method still maintains higher when more noises are added, generally 8%–10% higher than the other two methods. The proposed method also shows advantages in identifying multi-abnormity and reducing model establishing workload.

Developing an expert prognosis system of the reciprocating compressor based on associations among monitoring parameters and maintenance records

The reciprocating compressor is, in general, a critical equipment in a process plant. For certain ultra-high-pressure process, if the reciprocating compressor fails, often it will cause serious impact to not just the compressor itself, but also the process surrounds it. To prevent compressors from failures, an expert diagnosis system is needed. However, the traditional rule-based expert system is quite inefficient and difficult to create.For an expert prognosis system that is customized to meet needs of a specific process, one needs to refer to plant maintenance history, which is hard to come by due to the fact that most maintenance was poorly documented. This research attempt to demonstrate the feasibility of developing an expert prognosis system through implementation of association rules. Rather than mining from maintenance history, records of failure cases were collected from technical journal articles by extracting information containing failure symptoms and causes on failed components, that mimicking repair history. In total, 115 failure information out from 41 journal articles were gathered. Applications of this approach to practical use in a process plant is easy by replacing the failure information table with that from datamining the repair history. The failure information was first tabulated and then put through association analysis for support, confidence, and lift between two parameters. The demonstration program has been successful with 1-to-1, many-to-1, and many-to-many analysis among failed components, failure modes, and operation parameters.

Pemilihan Smartphone Berdasarkan Rekomendasi Profile User: Integrasi Fuzzy Analytical Hierarchy Process dan Rule Based

The usability and usefulness of smartphones have been found to lack optimality. Moreover, the action of the customer appears to behave as a consumptive user instead of buying following the basic needs. In selecting the right smartphone, this study provides an alternative option for buyers. The Fuzzy Analytical Hierarchy Process (F-AHP) approach is applied by distinguishing between two distinct decision-making namely, Profile User as a user recommendation-based, and Smartphone-based selection. The suggested requirements are hobbies, areas of jobs, and the use of social network applications in determining the user profile. The weights of the criteria and alternatives for profile users are acquired through the dissemination of questionnaires to 117 respondents. In the meantime, parameters such as Random-Access Memory (RAM), Read-Only Memory (ROM), camera, processor, screen, and battery are added to the smartphone list of criteria upon on the interviews of 15 experts and practices in smartphone and Information Technology (IT). F-AHP rates each of the Smartphone and User Profiles as a first-round output. A rule-based expert system is employed to intertwine Smartphone and User Profiles decision-making viewpoints. The relationship between the Smartphone and the User Profile is therefore circumscribed as the final decision to applaud the acceptable Smartphone for those users of the profile. A prototype of the MatchSmartPhone application has been developed to computerize the F-AHP calculation. This application can be used to assist users according to the user's profile in delivering the best Smartphone device recommendations. Users would be wiser and smarter when shopping in advance.

Parallel multipopulation optimization for belief rule base learning

As a rule-based expert system, the belief rule base (BRB) exhibits tremendous advantages in modeling nonlinearity for complex systems. Present BRB learning studies can be classified into three categories: BRB structure learning, BRB parameter learning, and BRB joint optimization but only in an iterative and separate fashion. In this study, a novel Parallel Multipopulation optimization approach for BRB, i.e., PMP-BRB, is proposed that simultaneously optimizes the structure and parameters of a BRB. In the optimization model of PMP-BRB, the structure of BRB, i.e., the number of rules, is used as another decisive variable. In the optimization algorithm of PMP-BRB, multiple populations are initialized and subsequently optimized, with different populations representing BRBs of varied sizes. Furthermore, a “completion and deletion” strategy is proposed, wherein individual BRBs in different populations are completed with additional genes that only engage in the optimization operations but not in fitness calculations. Moreover, a trade-off analysis is conducted for decision-makers to identify the final optimal configuration of a BRB based on their preference. The proposed PMP-BRB approach is validated by four cases, namely a numerical case, two practical cases, and a case study with five classification benchmarks. Four evolutionary algorithms are tested and compared. With the structure and parameters optimized simultaneously, all the four cases yield competitive results in comparison with those of previous studies.

The rapid development of knowledge bases using UML class diagrams

The knowledge bases design that employs conceptual models and their transformations is one of the most interesting areas of knowledge engineering and it remains critical. This paper presents an approach to the rapid development of knowledge bases for rule-based expert systems on the basis of the model-based generation of program codes. The approach proposed uses conceptual models in the form of UML class diagrams as a source of knowledge. In accordance with the technique that we propose, diagram concepts and relationships are extracted, mapped to the ontology of a subject domain and represented in the form of fact and rules templates. The original notation, namely the rule visual modelling language (RVML), is used to visualise and modify mapped elements. The CLIPS is used as the targeted knowledge base programming language. This approach does not eliminate errors due to conceptual models; but provides minimisation of programming errors resulting from hand coding.

Cloud computing and semantic web technologies for ubiquitous management of smart cities-related competences

One of the biggest challenges in building sustainable smart cities of the future is skill management and development. Developing the digital skills of the municipalities’ workforce is crucial for all occupational profiles and specifically for those that are actively involved in the development and operation of digital services for a smart city. In this context, the current paper presents a semantically enhanced cloud-based IT approach which integrates learning pathways with competence management of personnel working as management and technical employees in a smart city municipality. The proposed approach combines a rule-based expert system with a semantically rich infrastructure in order to map skill gap diagnosis to required learning objects and, thus, to contribute in developing appropriate competences of smart city professionals. The semantic infrastructure of the learning platform consists of an ontology which encapsulates the appropriate knowledge and a rule-base for modeling the steps of an employee training process. In order to achieve efficiency in competence management the semantic model has been transformed into a relational database schema, which is further utilized by the system for the execution of useful queries. The paper presents the key modeling artifacts of the proposed approach and the architecture of the implemented CMUTE system.

Sportif Faaliyetlerde Kullanılmak Üzere Bir Uzman Sistem Tasarımı

Vucut gelistirme gibi izotonik hareketlerin yapildigi sporlarda ve rehabilitasyon sureclerinde, antrenmanlarin eszamanli izlenmesi, yanlis yapilan hareketlerin aninda duzeltilebilmesi antrenman yeterliligi ve veriminin belirlenebilmesi bireylerin yaralanma risklerinden uzak ve saglikli antrenman yapabilmeleri acisindan hayati onem tasimaktadir. Calismamizda, bu amaca yonelik hareket modeline dayali kural tabanli bir Uzman Sistem (ES) tasarlanmistir. Tasarlanan sistem, antrenor uzerinden elde edilen LR (Lateral Raise) antrenmani verileri araciligiyla, DTW metodu ile karsilastirmali olarak test edilmistir. Test sonuclarinda, tasarladigimiz ES'in aldigi kararlarin DTW metodundan elde edilen kararlar ile karsilastirmasinda %64 oraninda dogrulukla, daha yuksek oranda dogruluga sahip oldugu gorulmustur.

A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error.

ObjectiveTo improve patient safety and clinical outcomes by reducing the risk of prescribing errors, we tested the accuracy of a hybrid clinical decision support system in prioritizing prescription checks.Materials and MethodsData from electronic health records were collated over a period of 18 months. Inferred scores at a patient level (probability of a patient’s set of active orders to require a pharmacist review) were calculated using a hybrid approach (machine learning and a rule-based expert system). A clinical pharmacist analyzed randomly selected prescription orders over a 2-week period to corroborate our findings. Predicted scores were compared with the pharmacist’s review using the area under the receiving-operating characteristic curve and area under the precision-recall curve. These metrics were compared with existing tools: computerized alerts generated by a clinical decision support (CDS) system and a literature-based multicriteria query prioritization technique. Data from 10 716 individual patients (133 179 prescription orders) were used to train the algorithm on the basis of 25 features in a development dataset.ResultsWhile the pharmacist analyzed 412 individual patients (3364 prescription orders) in an independent validation dataset, the areas under the receiving-operating characteristic and precision-recall curves of our digital system were 0.81 and 0.75, respectively, thus demonstrating greater accuracy than the CDS system (0.65 and 0.56, respectively) and multicriteria query techniques (0.68 and 0.56, respectively).DiscussionOur innovative digital tool was notably more accurate than existing techniques (CDS system and multicriteria query) at intercepting potential prescription errors.ConclusionsBy primarily targeting high-risk patients, this novel hybrid decision support system improved the accuracy and reliability of prescription checks in a hospital setting.

An Application of Knowledge Engineering to Mathematics Curricula Organization and Formal Verification

The authors present a theoretical proposal for the organization of mathematical contents, more precisely to curricula development formalization and formal verification, inspired by knowledge engineering techniques. The situation addressed is the following: the starting point is a mathematical “official curriculum” (or part of it), not necessarily completely detailed. In our proposal, a group of experts would have to first build a detailed formulation of this curriculum (including the “prerequisite” relation between contents), which we will denominate “preprocessed official curriculum.” We detail how any “official curriculum development” could then be rigorously formalized and formally verified in a way inspired by rule-based expert system formal verification. We have defined the following terms: “contents soundness,” “contents completeness,” “relation soundness,” “relation completeness,” and “absence of cycles.” We believe that this is a completely new formalization within mathematics teaching theory that, once computer is implemented, would be very helpful. That would be the case, for instance, in countries where government sets the “official curricula” for Primary and Secondary Education and textbook contents have to be manually checked and approved by academic authorities: evaluators would “only” have to extract the textbook contents and set the “prerequisite” relation among them and let the computer do the rest.

An intelligent and personalized course advising model for higher educational institutes

In this study, we present an intelligent personalized course advising model by analyzing, selecting, and modifying effective course advising technologies. It overcomes the existing course advising systems by using common design for all Higher Educational Institutes and making the rule, regulations, curriculum, and grading system more flexible to change without affecting persistent academic data. The proposed model helps students by analyzing and suggesting appropriate courses according to their academic history and special interest via harmonized knowledge of different experts. To develop this model, analyze various requirements were gathered and explicitly analyzed from the course advisory systems and other related technologies, that were used in seven institutes. The proposed model contains seven components, in which they use association rule, rule-based expert system, and fundamental recommendation algorithms according to their suitability for collected requirements. We made more modifications and enhancements on those algorithms to achieve the purpose of each component. We used ISO-25010 quality model with the institutional rules and regulations of three departments to evaluate the proposed model. The obtained results showed that the proposed methodology of the model is an effective way to improve the efficiency of students course advisory systems. It also indicates that advisors and students can save more time through effective and efficient course suggestions during the course registration period.