Rule Antecedents Research Articles

Solvability of systems of partial fuzzy relational equations revisited – a short note

Fuzzy inference systems have been widely investigated from different perspectives, including their logical correctness. It is not surprising that the logical correctness led mostly to the questions on the preservation of modus ponens. Indeed, whenever such a system processes an input equivalent to one of the rule antecedents, it is natural to expect the modus ponens to be preserved and the inferred output to be identical to the respective rule consequent. This leads to the related systems of fuzzy relational equations where the antecedent and consequent fuzzy sets are known values, the inference is represented either by the direct product related to the compositional rule of inference or by the Bandler-Kohout subproduct, and the fuzzy relation that represents the fuzzy rule base is the unknown element in the equations. The most important question is whether such systems are solvable, i.e., whether there even exists a fuzzy relation that models the given fuzzy rule base in such a way that the modus ponens is preserved.Partiality allows dealing with partially defined truth values which allows to deal with situations, where we cannot define the truth value for a given predicate. The partial logics have been recently extended to partial fuzzy logics and the partial fuzzy set theory has been developed. Partial fuzzy sets then may have undefined membership degrees for some values from the given universe.This background leads naturally to the problem of solvability of partial fuzzy relational equations, which are equations with partial fuzzy sets in the role of antecedents and consequents and with partial fuzzy relation as the model of the given fuzzy rule base. Such a setting led to a recent publication that uncovers the solvability and even the shape of the solutions. In this contribution, we revisit the problem and consider a specific case that allows partiality only in the input. In other words, antecedents, as well as consequents expressing the knowledge, are fully defined. Thus, the model of the fuzzy rules is one of the standard and fully defined relations. We investigate what happens if the input differs from one of the antecedents only by a few undefined values. This mimics the situation when a description of an observed object misses a few values in its feature vector. We show that under specific conditions, we still may preserve the modified modus ponens, i.e., that the inferred output is identical with the fully defined consequent of the respective rule.

Fuzzy models for cyber incident detection in military information and communication systems databases

Protecting databases of military information and communication systems is an extremely important task in the modern cybersecurity sphere. Growing threats from cyberattacks, the need to effectively detect, counteract and prevent them require the use of new, more effective models and methods. The main disadvantages of existing models and methods include insufficient sensitivity to new threats, a large number of detection errors, low response to new threats, the possibility of bypassing protective measures, and low scalability, which are key challenges for further improvement and development of cybersecurity. The article analyzes the existing fuzzy models for detecting cyber incidents, identifies their shortcomings and emphasizes the need for their further improvement and development. An improved fuzzy model for detecting cyber incidents in databases of military information and communication systems and an improved fuzzy model for detecting cyber incidents in databases of military information and communication systems with weighted rules based on the expansion of cyber incident signs by obtaining them from different levels of cyber security of the data are proposed. The main levels of database cybersecurity include: the operating system level, the network level, and the database management system level. To eliminate the shortcomings associated with the false triggering of cyber incident detection rules and the complexity of their configuration in a dynamically changing cyberattack landscape, as well as the dimensionality of the knowledge base of the information and security event management system, a fuzzy model for detecting cyber incidents in databases of military information and communication systems with weights of rule antecedents is proposed. The expediency of applying the developed model is shown.

A novel temporal adaptive fuzzy neural network for facial feature based fatigue assessment

When engaging in activities such as using video display terminals, driving, or sports, effective fatigue monitoring is crucial. Nevertheless, obtaining biological information through contact devices may interfere with normal activities. Deterministic expressions in mainstream machine learning have limitations in reflecting the continuous, dynamic, and fuzzy process of fatigue status. Additionally, fatigue is a cumulative process where the previous state impacts the assessment of the current one. To address these needs and challenges, this paper proposes a novel model based on facial videos called the temporal adaptive fuzzy neural network (TAFNN) for fatigue assessment. TAFNN utilizes an adaptive fuzzy neural network as its foundation and employs causal and dilated convolutions for the time information processing method to achieve time series extraction and fatigue assessment. It leverages facial physiological and motion features to minimize interference during assessment. Furthermore, TAFNN introduces a new calculation method for rule antecedents to enhance stability. Experimental results demonstrate that TAFNN effectively captures the cumulation and fuzziness of state changes, outperforming other widely adopted methods in both assessment ability and runtime performance. The improved rule antecedent calculation method successfully mitigates the issue of multiple memberships rapidly approaching zero after combination. Through 1000 repeated experiments, the enhanced method reduces TAFNN’s instability by 81.58%.

МЕТОДИКА ВИЯВЛЕННЯ КІБЕРІНЦИДЕНТІВ SIEM-СИСТЕМОЮ В БАЗАХ ДАНИХ ІНФОРМАЦІЙНО-КОМУНІКАЦІЙНИХ СИСТЕМ ВІЙСЬКОВОГО ПРИЗНАЧЕННЯ

With the integration of information and communication systems into military operations, the issue of their cyber defense is becoming increasingly important. The main target for cyberattacks are databases that mostly contain confidential information. One of the most effective approaches to ensuring cybersecurity of databases of military information and communication systems is to use the intelligent capabilities of a SIEM system. SIEM allows real-time monitoring, analysis and response to potential cyber incidents. The article proposes a methodology for detecting cyber incidents by a SIEM system in databases of military information and communication systems. The main emphasis is placed on the multi-level protection of databases, which includes protection at the operating system level, the database level and the database management system, as well as the network level of protection. In order to improve the efficiency of cyber incident detection at the level of SIEM data analysis, an improved methodology based on fuzzy logic is used. Improvement of the methodology is achieved by introducing weights of antecedents in fuzzy rules, which allows, in some cases, to identify cyber incidents more accurately compared to existing models and methods. The weights of the antecedents of fuzzy rules are determined using the method of pairwise comparisons based on the rankings made on the 9-point Saaty scale. A decision-making algorithm for identifying cyber incidents based on the analysis of fuzzy rules and the weights of their antecedents is presented. An example of calculating the weights of antecedents of fuzzy rules using the method of pairwise comparisons based on rank estimates is given.

Erratum to: A Study to Determine The Antecedents of The Prosocial Rule Breaking Behaviour: A Qualitative Practice for Hotel Businesses

Erratum to: A Study to Determine The Antecedents of The Prosocial Rule Breaking Behaviour: A Qualitative Practice for Hotel Businesses

Development of the method of general interpolation for Z-number-valued if-then rules

Rule interpolation-based methods are used when the rule base is sparse. This frequently being the case, as information relevant to real-world problems is not usually comprehensive. At the same time, relevant information is often characterized by both fuzziness and partial reliability. To deal with such kind of information, the concept of Z-number was introduced by Zadeh. This paper is devoted to an extension of the general interpolation method for fuzzy rules to the case of if-then rules with Z-number-valued antecedents and consequents. The proposed approach relies on the determination of the distance between the current observation vector and vectors of rules antecedents. By determining the distance between the current vector and the antecedents of the rules, decisions can be made based on the nearest antecedents. In this context, rule antecedents are vectors that represent certain conditions. The resulting output is computed as a weighted sum of rules consequents. Weighting factors are used to account for the importance of each rule in the interpolation. Weights of interpolations are found on the basis of mentioned distance values. The results of this study are aimed at developing an approach to decision-making in terms of Z-valued information. The method is characterized by relatively low computational complexith. Regarding the application of the proposed approach, the job satisfaction evaluation problem is considered. Consequently, the obtained results confirm the efficiency of the proposed approach. The proposed method can be a useful tool for decision-making in various applications, especially where high computational complexity is unacceptable or impractical

A Study to Determine The Antecedents of The Prosocial Rule Breaking Behaviour: A Qualitative Practice for Hotel Businesses

A Study to Determine The Antecedents of The Prosocial Rule Breaking Behaviour: A Qualitative Practice for Hotel Businesses

A Study to Determine The Antecedents of The Prosocial Rule Breaking Behaviour: A Qualitative Practice for Hotel Businesses

A Study to Determine The Antecedents of The Prosocial Rule Breaking Behaviour: A Qualitative Practice for Hotel Businesses

W-Infer-polation: Approximate reasoning via integrating weighted fuzzy rule inference and interpolation

Fuzzy rule-based inference systems facilitate approximate reasoning for decision-making under circumstances where knowledge is imprecisely characterised. Compositional rule of inference (CRI) and fuzzy rule interpolation (FRI) are two typical types of technique to implement fuzzy systems. Questions of when to apply which of these two fuzzy inference techniques is often addressed by checking whether there exist certain rules that can match given observations. Both techniques have been widely investigated to improve the performance of approximate reasoning, with increasingly more attention being paid to the development of systems where rule antecedent attributes are associated with measures of their relative significance or weights. Unfortunately, they are mostly done in a separate manner within their own fields, making it difficult to achieve accurate reasoning when both techniques are required simultaneously. This paper presents an innovative fuzzy inference mechanism, W-Infer-polation, which organically integrates both enhanced versions of CRI and FRI that are equipped with rule attribute weights. In particular, the individual weights of rule antecedents and consequent are generated using the given unweighted rules only, creating an attribute weighted rule base. From this, W-Infer-polation exploits the learned weights to guide the selection of weighted rules for rule firing in CRI and that of nearest weighted rules for FRI. A systematic comparative experimentation is also presented, demonstrating the efficacy of W-Infer-polation.

A Blockchain, Smart Contract and Data Mining Based Approach toward the Betterment of E-Commerce

E-commerce platforms have made our life easier and bought plenty of advantages too. However, due to fraud and scams, trust is a concern while buying products online. In this study, we proposed a blockchain-based architecture for the e-commerce sector where data mining technology is used to detect fraudulent users by generating precise and effective rules, and smart contracts are used for enforcement and functionality management within the blockchain network. Our data mining approach yielded a competitive accuracy, precision, recall, and f1-measure of over 99% compared to the state-of-the-art. High performing rules are further tested for ten simulated cycles under completely unseen data to test their rigidity in a real-time scenario. Details analysis of delineation (if any) in rule antecedents has also been analyzed for these cycles. Tested and selected rules are stored in smart contracts deployed within blockchain network for security and immutability. For ensuring strict order criteria maintenance, better return policy, authentic review and scam avoidance, two smart contracts have been written which implement seller reputation mechanism and authentic review maintenance while safeguarding the seller from intentional defaming too. Altogether, the goal of this study is to establish a balance between all the parties within an e-commerce platform so that everyone's right is protected, money is safe and resources are not exploited.

Confucian Democracy in East Asia: A Discussion of Sungmoon Kim’s Democracy After Virtue: Toward Pragmatic Confucian Democracy

Confucian Democracy in East Asia: A Discussion of Sungmoon Kim’s <i>Democracy After Virtue: Toward Pragmatic Confucian Democracy</i>

Building a Fuzzy Classifier Based on Whale Optimization Algorithm to Detect Network Intrusions

The quantity of network attacks and the harm from them is constantly increasing, so the detection of these attacks is an urgent task in the information security field. In this paper, we investigate an approach to building intrusion detection systems using a classifier based on fuzzy rules. The process of creating a fuzzy classifier based on a given set of input and output data can be presented as a solution to the problems of clustering, informative features selection, and the parameters of the rule antecedents optimization. To solve these problems, the whale optimization algorithm is used. The performance of algorithms for constructing a fuzzy classifier based on this metaheuristic is estimated using the KDD Cup 1999 intrusion detection dataset. On average, the resulting classifiers have a type I error of 0.92% and a type II error of 1.07%. The obtained results are also compared with the results of other classifiers. The comparison shows the competitiveness of the proposed method.

Unknown Disease Outbreaks Detection: A Pilot Study on Feature-Based Knowledge Representation and Reasoning Model.

Background: The outbreak of COVID-19 in 2019 has rapidly swept the world, causing irreparable loss to human beings. The pandemic has shown that there is still a delay in the early response to disease outbreaks and needs a method for unknown disease outbreak detection. The study's objective is to establish a new medical knowledge representation and reasoning model, and use the model to explore the feasibility of unknown disease outbreak detection.Methods: The study defined abnormal values with diagnostic significances from clinical data as the Features, and defined the Features as the antecedents of inference rules to match with knowledge bases, achieved in detecting known or emerging infectious disease outbreaks. Meanwhile, the study built a syndromic surveillance base to capture the target cases' Features to improve the reliability and fault-tolerant ability of the system.Results: The study combined the method with Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and early COVID-19 outbreaks as empirical studies. The results showed that with suitable surveillance guidelines, the method proposed in this study was capable to detect outbreaks of SARS, MERS, and early COVID-19 pandemics. The quick matching accuracies of confirmed infection cases were 89.1, 26.3–98%, and 82%, and the syndromic surveillance base would capture the Features of the remaining cases to ensure the overall detection accuracies. Based on the early COVID-19 data in Wuhan, this study estimated that the median time of the early COVID-19 cases from illness onset to local authorities' responses could be reduced to 7.0–10.0 days.Conclusions: This study offers a new solution to transfer traditional medical knowledge into structured data and form diagnosis rules, enables the representation of doctors' logistic thinking and the knowledge transmission among different users. The results of empirical studies demonstrate that by constantly inputting medical knowledge into the system, the proposed method will be capable to detect unknown diseases from existing ones and perform an early response to the initial outbreaks.

Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-Based Approach

Here, we propose an unsupervised fuzzy rule-based dimensionality reduction method primarily for data visualization. It considers the following important issues relevant to dimensionality reduction-based data visualization: (i) preservation of neighborhood relationships, (ii) handling data on a non-linear manifold, (iii) the capability of predicting projections for new test data points, (iv) interpretability of the system, and (v) the ability to reject test points if required. For this, we use a first-order Takagi-Sugeno type model. We generate rule antecedents using clusters in the input data. In this context, we also propose a new variant of the Geodesic c-means clustering algorithm. We estimate the rule parameters by minimizing an error function that preserves the inter-point geodesic distances (distances over the manifold) as Euclidean distances on the projected space. We apply the proposed method on three synthetic and three real-world data sets and visually compare the results with four other standard data visualization methods. The obtained results show that the proposed method behaves desirably and performs better than or comparable to the methods compared with. The proposed method is found to be robust to the initial conditions. The predictability of the proposed method for test points is validated by experiments. We also assess the ability of our method to reject output points when it should. Then, we extend this concept to provide a general framework for learning an unsupervised fuzzy model for data projection with different objective functions. To the best of our knowledge, this is the first attempt to manifold learning using unsupervised fuzzy modeling.

WITHDRAWN: Mining of negative association rules using correlate frequent pattern tree

Associative rule mining is the discovery of rules from the item set constraint that contain a set of specific items in antecedent rule and a class label. Frequent Pattern (FP-Tree) is associative rule mining method that doesn’t provide large set of candidate sets. Recently, the researchers are focusing on finding the infrequent item set leads to Negative Association Rule (NAR) mining. In this research, (FP-Tree) is added with the linkage table called Correlated Frequent Pattern Tree (CFPT) that allows the method to analyze the patterns with frequent items. The Coverage Co-efficient Measure (CCM) is applied in the FP-Tree with linkage table to identify the correlated item sets. In the mining process, CCM helps to remove irrelevant item set from the transaction. The developed IFP-Tree reduces the memory usage and the execution time in mining the frequent pattern. The four datasets have used to experiment the performance of proposed CFPT technique. The execution time of proposed CFPT method is the 0.043 s in frequent pattern mining and 0.075 s in infrequent pattern mining.

An advanced interpretable Fuzzy Neural Network model based on uni-nullneuron constructed from n-uninorms

This paper formulates a fuzzy logic neuron that uses n-uninorms to construct uni-nullneurons. A fuzzy neural network (FNN) composed of these neurons is easy to operate with nullnorms and uninorms at different times, which results in high accuracy of the model outputs and increases the flexibility in connecting the rule antecedents (enabling AND and OR connections within one rule). This, in turn, may allow experts/operators to extract more knowledge from data. The FNN uses a one-versus-rest classifier learning scheme for multi-class classification problems, where neuron activation levels construct the (indicator) regression matrix; this results in a non-linear regression by indicator, which can resolve the inherent class masking problem apparent in the linear case. We propose a specific neuron-selection strategy in the learning stage that applies Lasso to bootstrap replications in order to ensure that the rule base is as compact as possible and induced by a low number of neurons. To evaluate the new neuron acting in FNNs, we performed pattern classification, and regression tests. Compared with traditional FNN in the literature, our variant showed improved model accuracies for several high-dimensional real-world datasets in binary and multi-class classification and regression problems. Combined with the ability to generate human-readable rules, this offers the ability to generate parsimonious responses with a high degree of confidence.

A Decision Tree-Initialised Neuro-fuzzy Approach for Clinical Decision Support

Apart from the need for superior accuracy, healthcare applications of intelligent systems also demand the deployment of interpretable machine learning models which allow clinicians to interrogate and validate extracted medical knowledge. Fuzzy rule-based models are generally considered interpretable that are able to reflect the associations between medical conditions and associated symptoms, through the use of linguistic if-then statements. Systems built on top of fuzzy sets are of particular appealing to medical applications since they enable the tolerance of vague and imprecise concepts that are often embedded in medical entities such as symptom description and test results. They facilitate an approximate reasoning framework which mimics human reasoning and supports the linguistic delivery of medical expertise often expressed in statements such as ‘weight low’ or ‘glucose level high’ while describing symptoms. This paper proposes an approach by performing data-driven learning of accurate and interpretable fuzzy rule bases for clinical decision support. The approach starts with the generation of a crisp rule base through a decision tree learning mechanism, capable of capturing simple rule structures. The crisp rule base is then transformed into a fuzzy rule base, which forms the input to the framework of adaptive network-based fuzzy inference system (ANFIS), thereby further optimising the parameters of both rule antecedents and consequents. Experimental studies on popular medical data benchmarks demonstrate that the proposed work is able to learn compact rule bases involving simple rule antecedents, with statistically better or comparable performance to those achieved by state-of-the-art fuzzy classifiers.

FUZZY INFERENCE SYSTEMS BASE ON POLYNOMIALCONSEQUENTS OF FUZZY RULES

Various fuzzy inference systems that operate on the basis of polynomial consequents of fuzzy rules. As well as inference methods for such systems, in particular, Takagi-Sugeno fuzzy inference systems, their differences from other popular fuzzy systems, such as Mamdani systems, etc., are considered. The attention is focused on the features of the functioning of such systems both in the construction of elementary fuzzy systems. The Systems for which the calculation of the general logical conclusion involves intermediate levels of logical inference with many hierarchically interconnected blocks of fuzzy rules. Fuzzy sets of type 2 are considered, the membership index of which is a fuzzy term of the first type. This allows you to take into account the secondary fuzziness of linguistic concepts in the design of intelligent systems based on fuzzy inference. Fuzzy systems of the second type based on Takagi-Sugeno systems and the iterative Karnik-Mendel algorithm are considered to obtain a logical conclusion for fuzzy systems with the interval membership functions of the second type in the antecedents of fuzzy rules. The proposed procedure for lowering the order of fuzzy rules for higher-order Takagi-Sugeno fuzzy systems is described and justified. A fuzzy inference method for higher-order fuzzy systems based on the partition of a set of input variables is proposed. It is proposed to build a separate block of fuzzy rules for each of the input subspaces in the presence of a common polynomial. Which is a higher-order consequent, that reduces the total number of fuzzy rules in blocks.

FuzzyFeatureRank. Bringing order into fuzzy classifiers through fuzzy expressions

This work presents FuzzyFeatureRank, a new feature reduction method inspired on PageRank to reduce the dimensionality of the feature space in supervised classification problems. More precisely, as it relies on a weighted directed graph, it is ultimately inspired on TextRank, a PageRank based method that adds weights to the edges to express the strength of the connections between nodes. The method is based on dividing each original feature used to describe the data into a set of fuzzy predicates and then ranking all of them by their ability to differentiate among classes in the light of the training set. In order to do that, both the information gained by each predicate and their redundancy with other already selected predicates are taken into account. The fuzzy predicates with the best scores can then be used as a reduced input to construct fuzzy classifiers that consider only the preselected predicates to build the antecedents of the fuzzy rules. The novelty of the proposal relies on being an approach halfway between feature selection and feature extraction approaches, being able to improve the discrimination ability of the original features but preserving the interpretability of the new features in the sense that they are fuzzy expressions. The experimental results support the suitability of the proposal.

Structural optimization of fuzzy rule-based models: Towards efficient complexity management

The primary aim of this study is concerned with the structural optimization of data-driven fuzzy rule-based systems (FRBS), with the intent of their complexity management. This is accomplished in two ways: the first one involves a structuralization of the antecedents and the second one deals with a structuralization of the consequents of the fuzzy rules. More specifically, this study contributes to the complexity management of fuzzy models by focusing on (i) the efficient arrangement (reduction) of the input spaces over which the antecedents of rules are formed and (ii) allocating the orders of local polynomial functions across the consequents of the rules. The originality of the study comes with the flexibility of FRBS that is endowed by admitting variability of input spaces standing in the antecedents of different rules as well as the variability of orders of polynomials (local functions) forming the consequents of the rules.Particle swarm optimization (PSO) is guided by the root mean squared error (RMSE) accuracy criterion to realize the efficient arrangement of input spaces and an allocation of the orders of the individual polynomials. In this optimization process, the Fuzzy C-Means (FCM) algorithm is employed to create fuzzy sets in the antecedents of the rules, while the standard Least Square Error (LSE) criterion is minimized to determine the coefficients of the polynomials in the consequents. The performance of the proposed model is quantified using some numeric data, including both synthetic and machine learning datasets.