Conjunctive Rule Research Articles

Operational effectiveness evaluation based on the reduced conjunctive belief rule base

To address the issue of rule premise combination explosion in the construction of the traditional complete conjunctive belief rule base (BRB), this paper introduces an orthogonal design method to reduce the conjunctive BRB. The reasoning method based on reduced conjunctive BRB is designed with the help of the conversion technology from conjunctive BRB to disjunctive BRB. Finally, the operational mission effectiveness evaluation is taken as an example to verify the proposed method. The results show that the method proposed in this paper is feasible and effective.

Theory and Computations for the System of Integral Equations via the use of Optimal Auxiliary Function Method

This paper extends the application of Optimal Auxiliary Function Method (OAFM) to the system of integral equations. The system of Volterra integral equations of second kind are taken as test examples. The results obtained by proposed method are compared with different methods i.e., Biorthogonal systems in a Banach space Fixed point, the implicit Trapezoidal rule in conjunction with Newton's method, and Relaxed Monte Carlo method (RMCM). The results revealed that OAFM is more efficient, simple to apply, and fast convergent. The auxiliary functions used in the method control its convergence. The values of arbitrary constants involved in the auxiliary functions are calculated optimally using the method of least square

Impact of the Canadian CT head rule supplemented by the original published minimum inclusion criteria to assist emergency department clinicians’ assessment of patients presenting post fall from residential aged care: a retrospective audit

BackgroundA large number of CT brain (CTB) scans are ordered in the ED for older patients with a confirmed or possible head strike but no ongoing symptoms of a head injury. This study aimed to evaluate the effect of the Canadian CT head rule supplemented by the original published minimum inclusion criteria to assist clinician assessment of the need for CTB following minimal trauma fall in patients presenting from residential aged care facilities to a major metropolitan emergency department (ED).MethodsThis study was conducted as a pre- and post-intervention retrospective audit. The intervention involved implementation of a decision support tool to help clinicians assess patients presenting to the ED following a fall. The tool integrated the Canadian CT Head Rule (CCHR) in conjunction with a simplified set of inclusion criteria to help clinicians define a minimum threshold for a “minor head injury”. Outcome data pertaining to CT brain ordering practices and results were compared over symmetrical 3-month time periods pre- and post-intervention in 2 consecutive years.ResultsThe study included 233 patients in the pre-intervention arm and 241 in the post-intervention arm. Baseline demographics and clinical characteristics were similar in both groups. There was a 20% reduction in the total number of CTB scans ordered following tool implementation, with 134 (57.0%) scans in the pre-intervention group and 90 (37.3%) in the post-intervention group (p < 0.01). The diagnostic yield in the pre- and post-intervention groups was 3.7 and 5.6% respectively (p = 0.52). No variation was observed in medical management between groups, and no patients in either group underwent neurosurgical intervention.ConclusionsUse of the CCHR supplemented by the original published minimum inclusion criteria appeared to safely reduce the number of CTB scans performed in residential aged care facility residents presenting to an ED after a fall, with no associated adverse outcomes. A larger study across multiple centres is required to determine widespread efficacy and safety of this tool.

Modelling consideration heterogeneity in a two-stage conjunctive model

We propose a two-stage choice model in which decision makers first filter out alternatives (consideration stage) before choosing their preferred alternative among the considered options (choice stage). The model accounts for heterogeneity in consideration screening by allowing respondents to have different thresholds for accepting attribute levels. By utilizing a conjunctive consideration rule, the model can capture well-known non-compensatory screening heuristics. The decision stage is modelled with a compensatory utility model. We compare our two-stage approach with the mixed logit model on simulated choice data, and conclude that both models can be distinguished based on the pattern of opt-out responses they produce. If such responses are the result of screening behaviour, the two-stage model is always selected in favour of a single-stage model. In addition, we evaluated several models on empirical choice data concerning preferences towards cinemas. Our results show that the data is best explained by the proposed model, suggesting that 73% of the participants used a screening rule before making a final choice.

Scenario evolutionary analysis for maritime emergencies using an ensemble belief rule base

Maritime emergencies exhibit uncertainty and complex evolution in the process of development. Scenario evolutionary analysis can identify the development of maritime emergencies, which is essential for an effective response. This paper proposes a novel ensemble belief rule base model (Ensemble-BRB) for scenario evolutionary analysis of maritime emergencies. Specifically, multiple low-dimensional random subspaces are generated randomly by combining mutual information so as to avoid combinatorial explosion, and to reduce the interference of redundant information. Subsequently, each random subspace is developed into a BRB subsystem that can be used to solve multiple-output problems, and the parameters of each BRB subsystem are learned using a differential evolutionary algorithm. Then, evidential reasoning is employed to combine the reasoning results of each BRB subsystem rule. Furthermore, the reasoning results of each BRB subsystem are combined using a cautious conjunctive rule approach to obtain the final results. The scenario evolutionary analysis of the proposed model is demonstrated and validated using maritime accidents as a case study, and the experimental results show that the proposed model can be effectively implemented. Moreover, in comparison with other well-known methods, the proposed method demonstrates good interpretability, high accuracy, and an effective solution for combinatorial explosion.

A fuzzy-based medical system for pattern mining in a distributed environment: Application to diagnostic and co-morbidity

In this paper we have addressed the extraction of hidden knowledge from medical records using data mining techniques such as association rules in conjunction with fuzzy logic in a distributed environment. A significant challenge in this domain is that although there are a lot of studies devoted to analysing health data, very few focus on the understanding and interpretability of the data and the hidden patterns present within the data. A major challenge in this area is that many health data analysis studies have focussed on classification, prediction or knowledge extraction and end users find little interpretability or understanding of the results. This is due to the use of black-box algorithms or because the nature of the data is not represented correctly. This is why it is necessary to focus the analysis not only on knowledge extraction but also on the transformation and processing of the data to improve the modelling of the nature of the data. Techniques such as association rule mining and fuzzy logic help to improve the interpretability of the data and treat it with the inherent uncertainty of real-world data. To this end, we propose a system that automatically: a) pre-processes the database by transforming and adapting the data for the data mining process and enriching the data to generate more interesting patterns, b) performs the fuzzification of the medical database to represent and analyse real-world medical data with its inherent uncertainty, c) discovers interrelations and patterns amongst different features (diagnostic, hospital discharge, etc.), and d) visualizes the obtained results efficiently to facilitate the analysis and improve the interpretability of the information extracted. Our proposed system yields a significant increase in the compression and interpretability of medical data for end-users, allowing them to analyse the data correctly and make the right decisions. We present one practical case using two health-related datasets to demonstrate the feasibility of our proposal for real data.

DIAGNOSTIC Classification Analysis of Problem-Solving Competence using Process Data: An Item Expansion Method.

Process data refer to data recorded in computer-based assessments (CBAs) that reflect respondents' problem-solving processes and provide greater insight into how respondents solve problems, in addition to how well they solve them. Using the rich information contained in process data, this study proposed an item expansion method to analyze action-level process data from the perspective of diagnostic classification in order to comprehensively understand respondents' problem-solving competence. The proposed method cannot only estimate respondents' problem-solving ability along a continuum, but also classify respondents according to their problem-solving skills. To illustrate the application and advantages of the proposed method, a Programme for International Student Assessment (PISA) problem-solving item was used. The results indicated that (a) the estimated latent classes provided more detailed diagnoses of respondents' problem-solving skills than the observed score categories; (b) although only one item was used, the estimated higher-order latent ability reflected the respondents' problem-solving ability more accurately than the unidimensional latent ability estimated from the outcome data; and (c) interactions among problem-solving skills followed the conjunctive condensation rule, which indicated that the specific action sequence appeared only when a respondent mastered all required problem solving skills. In conclusion, the proposed diagnostic classification approach is feasible and promising analyzing process data.

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach.

False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures.

BR-FRL: A Belief Rule-Based Fault Recognition and Location Model for Bus Network Systems

For a control system with bus network topology, it is significant while difficult to accurately recognize and locate the fault of transmission medium. The reason is that the occurrence of one fault can lead to the abnormality of multiple terminal nodes. How to use these abnormal signals to realize effective fault recognition and fault location is the key to solving the problem. Inspired by the manual inference in engineering practice, this paper proposes a belief rule-based fault recognition and location model (BR-FRL) for transmission medium in bus network systems. The core idea is fault location based on the results of fault recognition. To adapt to the flexible expansion of bus topology, the designed BR-FRL model has self-organization characteristics. The highlights of this paper include three aspects: 1) The progressive reasoning of fault recognition and location based on belief rules is realized in one model; 2) A new model architecture is proposed, which integrates the belief rules, the group method of data handling, and the cautious conjunctive rule (CCR); 3) The parameter learning based on stochastic gradient descent is applied to BR-FRL, and the sensitivity analysis of evidence weight in CCR of BR-FRL is performed. The effectiveness of BR-FRL is verified by an experiment of gigabit-capable passive optical network.

Conjunctive Operation of Interbasin Transferred Water and Local Water in a Multisource Diversion-Supply-Allocation System

Interbasin water transfer (IBWT) projects are of great practical significance for alleviating water shortages due to the nonuniform spatiotemporal distribution of water resources. An appropriate multisource IBWT management model can facilitate decision making for effective conjunctive use of the transferred and local water resources. This study proposes a hierarchical-planning model (HPM) that includes three core modules—water diversion, supply, and allocation modules—that are sequentially incorporated and clearly reflect the hierarchical relation among the diversion-supply-allocation system in the context of a multisource IBWT. The HPM is decomposed under two-stage programming and resolved with a coordinated particle swarm algorithm. Its effectiveness was examined by applying it to the Han-to-Wei IBWT project in China. The results indicate that the conjunctive operation rules with the expression of water transfer and supply rule curves fully exploit the hydrological heterogeneity and mutual complementarity. Further, the proposed model achieves a trade-off of the multiple sources and successfully meets the joint demands of different sectors and regions. Specifically, the mirror image phenomenon that arises during the process of the conjunctive operation, especially in continuously dry periods, highlights the noticeable mutual effect of the transferred and local water to cover the gap between that and the joint demands. The proposed model is a promising tool for integrated diversion-supply-allocation management of a multisource IBWT system, and it can be expanded to other water resource management systems involving the whole or partial process of water transfer, water supply, and water allocation.

The relationship between purposiveness and information integration: The effect of metamotivational states on cognitive rules in sport

IntroductionInformation integration theory is based on an axiom of purposiveness (i.e. cognition is goal-motivated) and on an axiom of integration (i.e. an action or thought depends on the integration of several stimuli). The relationship between the two axioms has not previously been studied. ObjectiveTo explore the influence of metamotivational states on information integration. We looked at how 70 athletes combined four factors (autonomy, relatedness, competence, and the natural environment) when deciding whether or not to get involved in running under paratelic (activity-oriented) and telic (goal-oriented) conditions. MethodThe participants recorded their decision-making processes for two sets of 24 scenarios built from combinations of the four factors. Repeated-measures ANOVAs were applied to the data. ResultsAll four factors were taken into consideration by the participants when deciding whether or not to get involved in running. The participants used a conjunctive rule under the paratelic condition and an additive rule under the telic condition. ConclusionAn individual's metamotivational state may modify the cognitive algebra in information integration. Further research is required to confirm these initial findings.

An adaptive outlier removal aided k-means clustering algorithm

K-means is one of ten popular clustering algorithms. However, k-means performs poorly due to the presence of outliers in real datasets. Besides, a different distance metric makes a variation in data clustering accuracy. Improve the clustering accuracy of k-means is still an active topic among researchers of the data clustering community from outliers removal and distance metrics perspectives. Herein, a novel modification of the k-means algorithm is proposed based on Tukey’s rule in conjunction with a new distance metric. The standard Tukey rule is modified to remove the outliers adaptively by considering whether the data is distributed to the left, right or even to the input data's mean value. The elimination of outliers is applied in the proposed modification of the k-means before calculating the centroids to minimize the outliers' influences. Meanwhile, a new distance metric is proposed to assign each data point to the nearest cluster. In this research, the modified k-means significantly improves the clustering accuracy and centroids convergence. Moreover, the proposed distance metric's overall performance outperforms most of the literature distance metrics. This manuscript's presented work demonstrates the significance of the proposed technique to improve the overall clustering accuracy up to 80.57% on nine standard multivariate datasets.

Collaborative Sampling and Binary Local Output Generation for Distributed Blind Cooperative Spectrum Sensing

This paper studies the problem of developing an efficient signal sampling framework for distributed blind spectrum sensing, together with the corresponding local cognitive radio (CR) decision and fusion rules. A novel collaborative sampling and binary output (CSBO) generating scheme is proposed to overcome the disadvantage of traditional energy detectors for blind spectrum sensing, in which the signal-to-noise ratio (SNR) and level of noise power at the CRs are unknown and thus the decision threshold of local energy detectors cannot be specified. In designing the fusion rule in conjunction with the proposed CSBO scheme, both fixed-sample-size tests and sequential tests are considered. It is theoretically shown that the proposed CSBO scheme outperforms traditional blind cooperative energy detectors with uniform sampling schemes in terms of its ability to increase the amount of information acquired under a given number of samples. Simulation results confirm the theoretical analyses, and demonstrate that, compared with the fixed-sample-size test, the sequential test requires fewer observations on average to achieve comparable detection performance. The reduction in the expected sample size needed in the sequential CSBO test when compared to the fixed-sample-size CSBO can reach 50% when primary signal is present.

Research on the Natural Language Recognition Method Based on Cluster Analysis Using Neural Network

Withthe technological advent, the clustering phenomenon is recently being used in various domains and in natural language recognition. This article contributes to the clustering phenomenon of natural language and fulfills the requirements for the dynamic update of the knowledge system. This article proposes a method of dynamic knowledge extraction based on sentence clustering recognition using a neural network-based framework. The conversion process from natural language papers to object-oriented knowledge system is studied considering the related problems of sentence vectorization. This article studies the attributes of sentence vectorization using various basic definitions, judgment theorem, and postprocessing elements. The sentence clustering recognition method of the network uses the concept of prereliability as a measure of the credibility of sentence recognition results. An ART2 neural network simulation program is written using MATLAB, and the effect of the neural network on sentence recognition is utilized for the corresponding analysis. A postreliability evaluation indexing is done for the credibility of the model construction, and the implementation steps for the conjunctive rule sentence pattern are specifically introduced. A new method of structural modeling is utilized to generate the structured derivation relationship, thus completing the natural language knowledge extraction process of the object-oriented knowledge system. An application example with mechanical CAD is used in this work to demonstrate the specific implementation of the example, which confirms the effectiveness of the proposed method.

A Study on Human Learning Ability during Classification of Motion and Colour Visual Cues and Their Combination

Abstract The paper presents a study on the human learning process during the classification of stimuli, defined by motion and color visual cues and their combination. Because the classification dimension and the features that define each category are uncertain, we model the learning curves using Bayesian inference and more precisely the Normalized Conjunctive Consensus rule, and also on the base of the more efficient probabilistic Proportional Conflict Redistribution rule No 5 (pPCR5) defined within Dezert-Smarandache Theory (DSmT) of plausible and paradoxical reasoning. Our goal is to study how these rules succeed to model consistently both: human individual and group behaviour during the learning of the associations between the stimuli and the responses in categorization tasks varying by the amount of relevant stimulus information. The effect of age on this process is also evaluated.

Data mining techniques for classifying and predicting Teachers’ performance based on their evaluation reports

Background/Objectives: Teachers’ performance is a key bridge to ensure successful pedagogical and educational objectives. However, the evaluation of teachers’ performance has been used to be a manual and temperamental task for school principals. This traditional context limits the teachers’ engagement to develop his/her performance as well as the principle to predict the strengths and weaknesses attached. Hence, schools’ principals need to use initiative methods to evaluate the teachers’ performance. In this study, a comparative approach was developed to evaluate the teachers’ performance aiming at avoiding the potential biased and temperamental human behaves in the teacher’s evaluation process. Methods: It involves different Data Mining (DM) techniques to identify the key patterns that are driving the teachers’ performance evaluation process. Therefore, the proposed approach extracts several potential and influential indicators mined from a paper-based on teachers’ performance reports at the Directorate of Education/ Southern Ghawrs, along with some demographics variables. Several DM algorithms are used to analyze teachers’ performance reports and predict their performance, such as NB Tree, Naïve Bayes, and Conjunctive Rule methods. Findings: The experimental results show a significant prediction accuracy improvement by (33%) when applying NB Tree compared to Conjunctive rule, and (12%) when compared to Naïve Bayes techniques respectively. Keywords: Data mining; machine learning; teachers’ performance; evaluation reports; Jordan

Biogeography based optimization for mining rules to assess credit risk

SummaryFinancial institutions, by and large, rely on the use of machine learning techniques to improve the classic credit risk assessment model for reduction of costs, delivery of faster decisions, guaranteed credit collections, and risk mitigations. As such, several data mining and machine learning approaches have been developed for computation of credit scores over the last few decades. Moreover, the existing rule‐based classification algorithms tend to generate a number of rules with a large number of conditions in the antecedent part. However, these algorithms fail to demonstrate high predictive accuracy while balancing coverage and simplicity. Thus, it becomes quite a challenging task for the researchers to generate an optimal rule set with high predictive accuracy. In this paper, we present an effective rule based classification technique for the prediction of credit risk using a novel Biogeography Based Optimization (BBO) method. The novel BBO in the context of rule mining is named as locally and globally tuned biogeography based rule‐miner (LGBBO‐RuleMiner). This is applied for discovering optimal rule set with high predictive accuracy from the dataset containing both the categorical and continuous attributes. The performance of the proposed algorithm is compared against a variety of rule‐miners such as OneR (1R), PART, JRip, Decision Table, Conjunctive Rule, J48, and Random Tree, along with some meta‐heuristic based rule mining techniques by considering two credit risk datasets obtained from University of California, Irvine (UCI) repository. It is found from the comparative study that the proposed rule miner in ten independent runs of ten‐fold cross validation outperforms all of the aforesaid algorithms in terms of predictive accuracy, coverage, and simplicity.

Commutative-symmetrical elastic–plastic stretch-tensor products and their rates

The commutative-symmetrical elastic–plastic stretch-tensor product and the multiplicative Bilby-Kröner-Lee decomposition of a deformation gradient are compared to one another in particular with respect to the corresponding time derivatives and tensor rates. It turns out that these multiplicative deformation-tensor models differ with respect to the elastic response just for a Lagrangean point of view and coincide for an Eulerian perspective. The uniqueness of the constitutive equations requires symmetric plastic-flow rules for commutative-symmetrical elastic–plastic stretch-tensor products and non-symmetric plastic-flow rules for multiplicative Bilby-Kröner-Lee formulations. Further, the author discusses whether a plastic rotation tensor—implied from a non-symmetric plastic-flow rule—is appropriate for a proper finite-plasticity formulation of polycrystalline metals and whether at all a plastic rotation is a material-dependent property in this context. A proper elasto-plasticity/-inelasticity model may be constituted for a symmetric plastic-flow rule in conjunction with a commutative-symmetrical elastic–plastic stretch-tensor product, which inherently includes even the simultaneous modeling of finite-elastic and finite-plastic/-inelastic orthotropy.

Risk assessments and structured care interventions for prevention of foot ulceration in diabetes: development and validation of a prognostic model.

Diabetes-related foot ulcers give rise to considerable morbidity, generate a high monetary cost for health and social care services and precede the majority of diabetes-related lower extremity amputations. There are many clinical prediction rules in existence to assess risk of foot ulceration but few have been subject to validation. Our objectives were to produce an evidence-based clinical pathway for risk assessment and management of the foot in people with diabetes mellitus to estimate cost-effective monitoring intervals and to perform cost-effectiveness analyses and a value-of-information analysis. We developed and validated a prognostic model using predictive modelling, calibration and discrimination techniques. An overview of systematic reviews already completed was followed by a review of randomised controlled trials of interventions to prevent foot ulceration in diabetes mellitus. A review of the health economic literature was followed by the construction of an economic model, an analysis of the transitional probability of moving from one foot risk state to another, an assessment of cost-effectiveness and a value-of-information analysis. The effects of simple and complex interventions and different monitoring intervals for the clinical prediction rules were evaluated. The main outcome was the incidence of foot ulceration. We compared the new clinical prediction rules in conjunction with the most effective preventative interventions at different monitoring intervals with a 'treat-all' strategy. Data from an electronic health record for 26,154 people with diabetes mellitus in one Scottish health board were used to estimate the monitoring interval. The Prediction Of Diabetic foot UlcerationS (PODUS) data set was used to develop and validate the clinical prediction rule. We searched for eligible randomised controlled trials of interventions using search strategies created for Ovid® (Wolters Kluwer, Alphen aan den Rijn, the Netherlands), MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials. Randomised controlled trials in progress were identified via the International Standard Randomised Controlled Trial Number Registry and systematic reviews were identified via PROSPERO. Databases were searched from inception to February 2019. The clinical prediction rule was found to accurately assess the risk of foot ulceration. Digital infrared thermometry, complex interventions and therapeutic footwear with offloading devices were found to be effective in preventing foot ulcers. The risk of developing a foot ulcer did not change over time for most people. We found that interventions to prevent foot ulceration may be cost-effective but there is uncertainty about this. Digital infrared thermometry and therapeutic footwear with offloading devices may be cost-effective when used to treat all people with diabetes mellitus regardless of their ulcer risk. The threats to the validity of the results in some randomised controlled trials in the review and the large number of missing data in the electronic health record mean that there is uncertainty in our estimates. There is evidence that interventions to prevent foot ulceration are effective but it is not clear who would benefit most from receiving the interventions. The ulceration risk does not change over an 8-year period for most people with diabetes mellitus. A change in the monitoring interval from annually to every 2 years for those at low risk would be acceptable. Improving the completeness of electronic health records and sharing data would help improve our knowledge about the most clinically effective and cost-effective approaches to prevent foot ulceration in diabetes mellitus. This study is registered as PROSPERO CRD42016052324. This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 62. See the NIHR Journals Library website for further project information.

Sentiment analysis from travellers’ reviews using enhanced conjunction rule based approach for feature-specific evaluation of hotels

The evolution of the internet has steered an enormous amount of travel reviews of hotels on the web. People referring to these reviews are often overloaded and confused by the sheer amount of information available. Sentiment analysis techniques have been successful in aggregating the reviews, extracting their sentiments and thereby minimizing the information overload. But lacking in specific feature-based sentiment analysis has restricted customers in getting the actual scenario of hotels entirely. This paper presents a prospective design on lexicon-based approach for feature-based sentiment analysis of travel reviews on hotels or resorts. In particular, an enhanced form of conjuncture-based approach is proposed to segregate sentences into relevant clauses, identifying the feature and the sentiment value associated with it. Overall sentiment score for features like food, service, and location of a hotel is being calculated. The experiment results show significantly better accuracy and precision than the conventional text segregation and sentiment analysis methods, namely trigram and conjunction rule based approach.