Learning Vector Quantization-Based Fuzzy Rules Oversampling Method

Imbalanced datasets are common in practical applications,and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes.However,the creation of fuzzy rules typically depends on expert knowledge,which may not fully leverage the label information in training data and may be subjective.To address this issue,a novel fuzzy rule oversampling approach is developed based on the learning vector quantization(LVQ)algorithm.In this method,the label information of the training data is utilized to determine the antecedent part of If-Then fuzzy rules by dynamically dividing attribute intervals using LVQ.Subsequently,fuzzy rules are generated and adjusted to calculate rule weights.The number of new samples to be synthesized for each rule is then computed,and samples from the minority class are synthesized based on the newly generated fuzzy rules.This results in the establishment of a fuzzy rule oversampling method based on LVQ.To evaluate the effectiveness of this method,comparative experiments are conducted on 12 publicly available imbalance datasets with five other sampling techniques in combination with the support function machine.The experimental results demonstrate that the proposed method can significantly enhance the classification algorithm across seven performance indicators,including a boost of 2.15%to 12.34%in Accuracy,6.11%to 27.06%in G-mean,and 4.69%to 18.78%in AUC.These show that the proposed method is capable of more efficiently improving the classification performance of imbalanced data.

Semantic Consistency and Correctness Verification of Digital Traffic Rules

The consensus of the automotive industry and traffic management authorities is that autonomous vehicles must follow the same traffic laws as human drivers.Using formal or digital methods,natural language traffic rules can be translated into machine language and used by autonomous vehicles.In this paper,a translation flow is designed.Beyond the translation,a deeper examination is required,because the semantics of natural languages are rich and complex,and frequently contain hidden assumptions.The issue of how to ensure that digital rules are accurate and consistent with the original intent of the traffic rules they represent is both significant and unresolved.In response,we propose a method of formal verification that combines equivalence verification with model checking.Reasonable and reassuring digital traffic rules can be obtained by utilizing the proposed traffic rule digitization flow and verification method.In addition,we offer a number of simulation applications that employ digital traffic rules to assess vehicle violations.The experimental findings indicate that our digital rules utilizing metric temporal logic(MTL)can be easily incorporated into simulation platforms and autonomous driving systems(ADS).

Density Clustering Algorithm Based on KD-Tree and Voting Rules

Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.

Improved STNModels and Heuristic Rules for Cooperative Scheduling in Automated Container Terminals

Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.

Heterogeneous information fusion recognition method based on belief rule structure

To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion,but the expert knowledge is not fully utilized,a heterogeneous informa-tion fusion recognition method based on belief rule structure is proposed.By defining the continuous probabilistic hesitation fuzzy linguistic term sets(CPHFLTS)and establishing CPHFLTS distance measure,the belief rule base of the relationship between feature space and category space is constructed through information integration,and the evidence reasoning of the input samples is carried out.The experimental results show that the proposed method can make full use of sensor data and expert knowledge for recognition.Compared with the other methods,the proposed method has a higher correct recognition rate under different noise levels.

Principles of Shiology——Revealing the Basic Rules of Human Shiance

The objective principles of shiology are mainly reflected in three fields as food acquisition, eaters' health and shiance order. Most of the objective principles in the field of food acquisition have been revealed by agronomy and foodstuff science. This research mainly focuses on 10 principles in the field of eaters' health and shiance order and in addition, there are also five lemmas that extend from the above principles. The 10 principles are the core theory of the shiology knowledge system, which play an important role in the objective principles revealed by human beings and constitute one of the basic principles of human civilization. Compared with the scientific principles of mathematics, physics, chemistry and economics, the principles of shiology have three characteristics as popularity, practicability and survivability. The principles of shiology in the field of eaters' health are all around us, and everyone can understand and master them. Using the principles of shiology can improve the healthy life span of 8 billion people. The principles of shiology in the field of shiance order is an important tool of social governance, which can reduce human social conflicts, reduce social involution, improve overall efficiency of social operation, and maintain the sustainable development of human beings.

Discovering hidden patterns:Association rules for cardiovascular diseases in type 2 diabetes mellitus

BACKGROUND It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus(T2DM)and coronary artery disease(CAD),and studies are able to correlate their relationships with available biological and clinical evidence.The aim of the current study was to apply association rule mining(ARM)to discover whether there are consistent patterns of clinical features relevant to these diseases.ARM leverages clinical and laboratory data to the meaningful patterns for diabetic CAD by harnessing the power help of data-driven algorithms to optimise the decision-making in patient care.AIM To reinforce the evidence of the T2DM-CAD interplay and demonstrate the ability of ARM to provide new insights into multivariate pattern discovery.METHODS This cross-sectional study was conducted at the Department of Biochemistry in a specialized tertiary care centre in Delhi,involving a total of 300 consented subjects categorized into three groups:CAD with diabetes,CAD without diabetes,and healthy controls,with 100 subjects in each group.The participants were enrolled from the Cardiology IPD&OPD for the sample collection.The study employed ARM technique to extract the meaningful patterns and relationships from the clinical data with its original value.RESULTS The clinical dataset comprised 35 attributes from enrolled subjects.The analysis produced rules with a maximum branching factor of 4 and a rule length of 5,necessitating a 1%probability increase for enhancement.Prominent patterns emerged,highlighting strong links between health indicators and diabetes likelihood,particularly elevated HbA1C and random blood sugar levels.The ARM technique identified individuals with a random blood sugar level>175 and HbA1C>6.6 are likely in the“CAD-with-diabetes”group,offering valuable insights into health indicators and influencing factors on disease outcomes.CONCLUSION The application of this method holds promise for healthcare practitioners to offer valuable insights for enhancing patient treatment targeting specific subtypes of CAD with diabetes.Implying artificial intelligence techniques with medical data,we have shown the potential for personalized healthcare and the development of user-friendly applications aimed at improving cardiovascular health outcomes for this high-risk population to optimise the decision-making in patient care.

Comparison of Block Design Nonparametric Subset Selection Rules Based on Alternative Scoring Rules

This article compares the size of selected subsets using nonparametric subset selection rules with two different scoring rules for the observations. The scoring rules are based on the expected values of order statistics of the uniform distribution (yielding rank values) and of the normal distribution (yielding normal score values). The comparison is made using state motor vehicle traffic fatality rates, published in a 2016 article, with fifty-one states (including DC as a state) and over a nineteen-year period (1994 through 2012). The earlier study considered four block design selection rules—two for choosing a subset to contain the “best” population (i.e., state with lowest mean fatality rate) and two for the “worst” population (i.e., highest mean rate) with a probability of correct selection chosen to be 0.90. Two selection rules based on normal scores resulted in selected subset sizes substantially smaller than corresponding rules based on ranks (7 vs. 16 and 3 vs. 12). For two other selection rules, the subsets chosen were very close in size (within one). A comparison is also made using state homicide rates, published in a 2022 article, with fifty states and covering eight years. The results are qualitatively the same as those obtained with the motor vehicle traffic fatality rates.

Verification and Application Evaluation of Intelligent Audit Rules for The UN9000 Urine Analysis System

Objective:To apply and verify the application of intelligent audit rules for urine analysis by Cui et al.Method:A total of 1139 urine samples of hospitalized patients in Tai’an Central Hospital from September 2021 to November 2021 were randomly selected,and all samples were manually microscopic examined after the detection of the UN9000 urine analysis line.The intelligent audit rules(including the microscopic review rules and manual verification rules)were validated based on the manual microscopic examination and manual audit,and the rules were adjusted to apply to our laboratory.The laboratory turnaround time(TAT)before and after the application of intelligent audit rules was compared.Result:The microscopic review rate of intelligent rules was 25.63%(292/1139),the true positive rate,false positive rate,true negative rate,and false negative rate were 27.66%(315/1139),6.49%(74/1139),62.34%(710/1139)and 3.51%(40/1139),respectively.The approval consistency rate of manual verification rules was 84.92%(727/856),the approval inconsistency rate was 0%(0/856),the interception consistency rate was 12.61%(108/856),and the interception inconsistency rate was 0%(0/856).Conclusion:The intelligence audit rules for urine analysis by Cui et al.have good clinical applicability in our laboratory.

Review of Arbitral Award of Network Loan and Analysis of Construction of Implementation Review Rules

The network arbitration cases arising from the network lending disputes are pouring into the courts in large numbers.It is reported that the network arbitration system of some arbitration institutions even“can accept more than 10,000 cases every day,”while online lending is booming,it has also caused a lot of contradictions and disputes,and traditional dispute resolution methods have failed to effectively respond to the need for efficient and convenient resolution of online lending disputes.This paper tries to study the arbitral award of online loans and proposes the construction of implementation review rules.

An analysis method of topological relations between Snort rules

It is difficult to knowall the relations between Snort rules. To deal with this problem, the topological relations between Snort rules are classified based on the set theory, and a method for calculating the topological relations between Snort rules is proposed. In the existing methods for analyzing the relations of Snort rules, the relations are usually determined only according to the header information of the Snort rules. Without considering the actions of Snort rules, the proposed method improves upon the existing methods and it can classify and calculate the topological relations between Snort rules according to both headers and options information of Snort rules. In addition, the proposed method is implemented by the functional language Haskell. The experimental results showthat the topological relations between Snort rules can be calculated rapidly and effectively. The proposed method also provides an important basis for conflict detection in the succeeding Snort rules.

Exploring the rules of cropland elevation uplift in China

Cropland elevation uplift(CLEU) has recently become a new challenge for agricultural modernization,food security,and sustainable cropland use in China.Uncovering the rules of CLEU is of great theoretical and practical significance for China’s sustainable agricultural development and rural revitalization strategy.However,existing studies lack in-depth disclosure of multi-scale CLEU evolution rules,making it difficult to support the formulation of specific cropland protection policies.We analyzed the spatio-temporal evolution and multiscale CLEU in China from 1980 to 2020 using the Lorenz curve,gravity center model,hotspot analysis,and cropland elevation spectrum.The results indicated that the center of gravity of cropland moved to the northeast from 1980 to 2000 and then shifted to the northwest.The spatial distribution of cropland became increasingly imbalanced from 1980 to 2000.The change hotspots clustered in the northwest and the northeast,whereas cold-spots were mainly in southeastern China.The average elevation of cropland increased by 17.38 m,and the elevation uplift rule in different regions differed evidently across scales.From 1980 to 2000,all provinces except Xinjiang,Inner Mongolia,Gansu,and Yunnan exhibited CLEU,with Qinghai,Tibet,Beijing,and Guangdong showing the most noticeable uplifting.The CLEU can alleviate the shortage of cropland to some extent.However,without a planning constraint,the CLEU will lead to the increase of ecological risk and food security risk.

基于ER Rule的多分类器汽车评论情感分类研究

该文针对汽车评论语料的情感二分类问题,提出一种基于证据推理规则的多分类器融合的情感分类方法。在情感特征构建方面,通过实验对比不同特征模型对分类结果的影响,并改进传统的TFIDF权重计算方法。同时,在此基础上使用ER Rule融合不同分类器进行文本情感极性分析,并考虑各分类器的权重和可靠度。最后,爬取汽车网站上的评论数据对上述方法进行测试,并用公开的中文酒店评论语料数据进行了验证,结果表明该方法能够有效集成不同分类器的优点,与传统机器学习分类算法相比,其结果在Recall,F1值和Accuracy三个指标上得到了提高,与目前流行的深度学习算法和集成学习算法相比,其结果总体占优。

A Novel Belief Rule-Based Fault Diagnosis Method with Interpretability

Fault diagnosis plays an irreplaceable role in the normal operation of equipment.A fault diagnosis model is often required to be interpretable for increasing the trust between humans and the model.Due to the understandable knowledge expression and transparent reasoning process,the belief rule base(BRB)has extensive applications as an interpretable expert system in fault diagnosis.Optimization is an effective means to weaken the subjectivity of experts in BRB,where the interpretability of BRB may be weakened.Hence,to obtain a credible result,the weakening factors of interpretability in the BRB-based fault diagnosis model are firstly analyzed,which are manifested in deviation from the initial judgement of experts and over-optimization of parameters.For these two factors,three indexes are proposed,namely the consistency index of rules,consistency index of the rule base and over-optimization index,tomeasure the interpretability of the optimizedmodel.Considering both the accuracy and interpretability of amodel,an improved coordinate ascent(I-CA)algorithmis proposed to fine-tune the parameters of the fault diagnosis model based on BRB.In I-CA,the algorithm combined with the advance and retreat method and the golden section method is employed to be one-dimensional search algorithm.Furthermore,the random optimization sequence and adaptive step size are proposed to improve the accuracy of the model.Finally,a case study of fault diagnosis in aerospace relays based on BRB is carried out to verify the effectiveness of the proposed method.

The study of slip line field and upper bound method based on associated flow and non-associated flow rules

At present, associated flow rule of traditional plastic theory is adopted in the slip line field theory and upper bound method of geotechnical materials. So the stress characteristic line conforms to the velocity line. It is proved that geotechnical materials do not abide by the associated flow rule. It is impossible for the stress characteristic line to conform to the velocity line. Generalized plastic mechanics theoretically proved that plastic potential surface intersects the Mohr-Coulomb yield surface with an angle, so that the velocity line must be studied by non-associated flow rule. According to limit analysis theory, the theory of slip line field is put forward in this paper, and then the ultimate beating capacity of strip footing is obtained based on the associated flow rule and the non-associated flow nile individually. These two results are identical since the ultimate bearing capacity is independent of flow role. On the contrary, the velocity fields of associated and non-associated flow rules are different which shows the velocity field based on the associat- ed flow rule is incorrect.

Influences of nonassociated flow rules on seismic bearing capacity factors of strip footing on soil slope by energy dissipation method

Seismic bearing capacity factors of a strip footing placed on soil slope were determined with both associated and nonassociated flow rules. Quasi-static representation of earthquake effects using a seismic coefficient concept was adopted for seismic bearing capacity calculations. A multi-wedge translational failure mechanism was used to obtain the seismic bearing capacity factors for different seismic coefficients and various inclined angles. Employing the associated flow rule, numerical results were compared with the published solutions. For bearing capacity factors related to cohesion and equivalent surcharge load, the maximum difference approximates 0.1%. However, the difference of bearing capacity factor related to unit weight is larger. With the two flow rules, the seismic bearing capacity factors were presented in the form of design charts for practical use. The results show that seismic bearing capacity factors related to the cohesion, the equivalent surcharge load and the unit weight increase greatly as the dilatancy angle increases, and that the nonassociated flow rule has important influences on the seismic bearing capacity.

Research on knowledge acquisition method about the IF/THEN rules based on rough set theory

The basic principles of IF/THEN rules in rough set theory are analyzed first, and then the automatic process of knowledge acquisition is given. The numerical data is qualitatively processed by the classification of membership functions and membership degrees to get the normative decision table. The regular method of relations and the reduction algorithm of attributes are studied. The reduced relations are presented by the multi-representvalue method and its algorithm is offered. The whole knowledge acquisition process has high degree of automation and the extracted knowledge is true and reliable.

Rules Mining-Based Gene Expression Programming for the Multi-Skill Resource Constrained Project Scheduling Problem

Themulti-skill resource-constrained project scheduling problem(MS-RCPSP)is a significantmanagement science problem that extends from the resource-constrained project scheduling problem(RCPSP)and is integrated with a real project and production environment.To solve MS-RCPSP,it is an efficient method to use dispatching rules combined with a parallel scheduling mechanism to generate a scheduling scheme.This paper proposes an improved gene expression programming(IGEP)approach to explore newly dispatching rules that can broadly solve MS-RCPSP.A new backward traversal decoding mechanism,and several neighborhood operators are applied in IGEP.The backward traversal decoding mechanism dramatically reduces the space complexity in the decoding process,and improves the algorithm’s performance.Several neighborhood operators improve the exploration of the potential search space.The experiment takes the intelligent multi-objective project scheduling environment(iMOPSE)benchmark dataset as the training set and testing set of IGEP.Ten newly dispatching rules are discovered and extracted by IGEP,and eight out of ten are superior to other typical dispatching rules.

A New Method Based on Association Rules Mining and Geo-filter for Mining Spatial Association Knowledge

Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining(GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%–70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.

Assessment of Patentability of Inventions Relating to "Rules and Methods for Mental Activities"

Examination of whether an invention is a patentable subject matter or not is one of the main parts of the patent examination. In the recently revised Guidelines for Examination a relatively major change has been made of the benchmark for the examination of inventions relating to "rules and methods for mental activities", for example, limiting the scope of application of Article 25.1 (2) of the Patent Law. As a result, the hitherto non-patentable subject matters are still excluded from patentability. That...