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.

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.

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.

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.

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。

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.

Attribute Reduction for Information Systems via Strength of Rules and Similarity Matrix

An information system is a type of knowledge representation,and attribute reduction is crucial in big data,machine learning,data mining,and intelligent systems.There are several ways for solving attribute reduction problems,but they all require a common categorization.The selection of features in most scientific studies is a challenge for the researcher.When working with huge datasets,selecting all available attributes is not an option because it frequently complicates the study and decreases performance.On the other side,neglecting some attributes might jeopardize data accuracy.In this case,rough set theory provides a useful approach for identifying superfluous attributes that may be ignored without sacrificing any significant information;nonetheless,investigating all available combinations of attributes will result in some problems.Furthermore,because attribute reduction is primarily a mathematical issue,technical progress in reduction is dependent on the advancement of mathematical models.Because the focus of this study is on the mathematical side of attribute reduction,we propose some methods to make a reduction for information systems according to classical rough set theory,the strength of rules and similarity matrix,we applied our proposed methods to several examples and calculate the reduction for each case.These methods expand the options of attribute reductions for researchers.

Proposing Adjustments to the Dry Season Inter-Reservoir Operation Rules of the Red River System to Improve Water Use Efficiency of the Reservoirs

The Red-Thai Binh River system is an important water resource to the Northern Delta, serving the development of agriculture, people’s livelihood and other economic sectors through its upstream reservoirs and a system of water abstraction works along the rivers. However, due to the impact of climate change and pressure from socio-economic development, the operation of the reservoir system according to Decision No. 740/QD-TTg was issued on June 17, 2019 by the Prime Minister of Government promulgating the Red-Thai Binh River system inter-reservoir operation rules (Operation rules 740) has some shortcomings that need adjustments for higher water use efficiency, meeting downstream water demand and power generation benefits. Through the results of water balance calculation and analysis of economic benefits from water use scenarios, this research proposed adjustment to the inter-reservoir operation during dry season in the Red River system. The result showed that an average water level of 1.0 - 1.7 m should be maintained at Hanoi during the increased release period.

An Intelligent Medical Expert System Using Temporal Fuzzy Rules and Neural Classifier

As per World Health Organization report which was released in the year of 2019,Diabetes claimed the lives of approximately 1.5 million individuals globally in 2019 and around 450 million people are affected by diabetes all over the world.Hence it is inferred that diabetes is rampant across the world with the majority of the world population being affected by it.Among the diabetics,it can be observed that a large number of people had failed to identify their disease in the initial stage itself and hence the disease level moved from Type-1 to Type-2.To avoid this situation,we propose a new fuzzy logic based neural classifier for early detection of diabetes.A set of new neuro-fuzzy rules is introduced with time constraints that are applied for thefirst level classification.These levels are further refined by using the Fuzzy Cognitive Maps(FCM)with time intervals for making thefinal decision over the classification process.The main objective of this proposed model is to detect the diabetes level based on the time.Also,the set of neuro-fuzzy rules are used for selecting the most contributing values over the decision-making process in diabetes prediction.The proposed model proved its efficiency in performance after experiments conducted not only from the repository but also by using the standard diabetic detection models that are available in the market.

Compatibility Rules of Prescriptions of Tibetan Medicine Polygonatum cirrhifolium Based on Data Mining

[Objectives]To study the prescription compatibility rules of the Tibetan medicine Polygonatum cirrhifolium(Polygonati Rhizoma,Huangjing)based on data mining,so as to provide data support for clinical application and new drug development.[Methods]Prescriptions containing Tibetan medicine Huangjing were collected from Tibetan Medicine Prescriptions Encyclopedia and Tibetan Medicine Classic Literature Collection.The Traditional Chinese Medicine Inheritance Computing System(TCMICS 3.0)and R version 4.1.3 were used to mine and analyze the compatibility rules of prescriptions containing Tibetan medicinal material Huangjing,the rules of medication for high-frequency diseases,and the association rules of various medicinal materials.[Results]A total of 124 prescriptions were collected from the classic literature of Tibetan medicine,and the frequency statistical analysis showed that drugs compatible with Huangjing(≥15)included Tianmendong(Root of Cochinchinese Asparagus)(80),Mirabilis himalaica(Edgew.)Heimerl(Himalayan purple jasmine)(72),Jili(Tribuli Fructus)(70),Tibetan Aoruqin(Vicatia coniifolia)(58),Baidoukou(Round Cardamom Fruit)(47),Hezi(Chebulae Fructus)(45),Shouzhangshen(Rhizome of Conic Gymnadenia)(28),Yuganzi(Phyllanthi Fructus)(26),Pomegranate(25),Maohezi(Terminaliae Belliricae Fructus)(24),Zicaorong(Lacca)(23),Rougui(Cinnamomi Cortex)(15).The main diseases were yellow water disease(20),gynecological diseases(19),kidney cold disease(17),and other Tibetan medicine diseases.Association rule analysis showed that the drug combinations with the highest frequency were Huangjing—Tianmendong,Huangjing—Himalayan purple jasmine,and Huangjing—Jili.[Conclusions]The frequency of combined use of Tibetan medicine Huangjing with dry yellow water,tonifying kidney and diuresis,nourishing health,and treating gynecological diseases is relatively high.

Study on medication rules of Chinese herbs in the regulation of necroptosis based on network pharmacology and data mining

Objective:To analyze the basis and medication rules of Chinese herbs in the regulation of necroptosis.Methods:With the help of GeneCards,DrugBank,TTD,DisGeNET,OMIM database to collect the action targets of necroptosis,the TCMSP database to obtain the target‑related compounds and Chinese herbs,and the ADME criteria and Lipinski rule as the conditions for screening,to build the target‑compound,target‑compound‑Chinese herbs network.The information of Chinese herbal medicine's sexual taste and meridian was collected,and the drug use pattern was analyzed.The information on the property,flavor and channel tropism of Chinese herbs was collected to analyze the medication laws.Molecular docking of core targets and compounds in the network with AutoDockTools software,and PyMOL software was used to display the combinations with good docking results.Results:A total of 12 potential targets acting on necroptosis were obtained,matching to 191 candidate compounds and 366 herbal medicines.Quercetin,wogonin,triptolide,licochalcone a,ellipticine are more important and may be the main small molecule substances underlying the regulation of necroptosis.The more important Chinese herbs are Licorice,Forsythia,Salivae Miltiorrhizae,Ginkgo Leaf,Eucommia ulmoides Oliv,etc.The herbal medicines are mainly bitter and pungent,with cold and warm taste,which were attributed to the liver and lung meridians.BCL2‑beta‑sitosterol、MAPK14‑luteolin、MAPK14‑formononetin、TP53‑formononetin are better molecular docking results,which have strong docking activity.Conclusion:The study systematically analyzes the material basis of regulating necroptosis and summarizes the general rule of regulating necroptosis in Chinese medicine,which provides ideas for clinical development of agents to interfere with necroptosis.

Compatibility Rules of Prescriptions of Tibetan Medicinal Material Mirabilis himalaica(Edgew.)Heimerl Based on Data Mining

[Objectives]To study the prescription compatibility rules of the Tibetan medicinal material Mirabilis himalaica(Edgew.)Heimerl(Himalayan purple jasmine)based on data mining,and to provide reference for clinical application and new drug development.[Methods]The literature data were collected and the prescriptions containing Himalayan purple jasmine were sorted and classified by Microsoft Excel 2019 software.The Traditional Chinese Medicine Inheritance Computing System(TCMICS 3.0)was used to statistically analyze the high-frequency drugs and core drug combinations,the frequency of disease treatment,and the rules of"tastes transforming flavors"of drugs.The FP-tree algorithm was used to analyze the association rules among various Tibetan medicinal materials in the prescriptions,and different supports were set to analyze the compatibility rules of the prescriptions.[Results]There were a total of 129 prescriptions containing Himalayan purple jasmine,297 medicinal materials used in combination with it,and 34 Tibetan medicinal materials with a frequency of≥15.The medicinal flavors were mostly sweet,bitter,and pungent.Among the three flavors,bitterness and sweetness were the majority.At the same time,the medicinal properties were blunt,soft,cool,and warm.A total of 130 kinds of diseases were involved,among which 8 kinds of diseases such as gynecology,kidney cold and yellow water disease had a high frequency.Through association rule analysis,33 commonly used core drug combinations were obtained.The support,confidence,and frequency of the five drug combinations[Jili(Tribuli Fructus),Huangjing(Polygonati Rhizoma),Tianmendong(Root of Cochinchinese Asparagus),and Tibetan Aoruqin(Vicatia coniifolia)]→Himalayan purple jasmine were the basic prescription of Tibetan medicine Wudagen Powder.The drug combinations with higher confidence included Bibo(Piperis Longi Fructus)→Himalayan purple jasmine,Pomegranate(Punica granatum→Himalayan purple jasmine,Baidoukou(Round Cardamom Fruit)→Himalayan purple jasmine,Hezi(Chebulae Fructus)→Himalayan purple jasmine.Jili(Tribuli Fructus)→Himalayan purple jasmine,Huangjing(Polygonati Rhizoma)→Himalayan purple jasmine and Tianmendong(Root of Cochinchinese Asparagus)→Himalayan purple jasmine had highest frequency.[Conclusions]Through the TCMICS,this paper analyzed the compatibility rules of commonly used drugs containing Himalayan purple jasmine and the characteristics of the main diseases.It can be seen that Himalayan purple jasmine is sweet in taste and warm in nature.It assists sovereign drugs in the form of ministerial drugs,which enhances the effect of regulating"long"and nourishing,and plays the role of regulating"long",dispelling cold,and reconciling various drugs,which embodies the compatibility rules of sovereign and ministerial drugs of Tibetan medicines.The main diseases of Himalayan purple jasmine are related to"long"regulation,and it is mostly used in the treatment of gynecological diseases after being combined with other drugs to form a prescription.Most drugs in the prescriptions are sweet,which is the therapeutic principle of regulating"long"and nourishing,and verifies the scientific and rational principle of clinical use of Tibetan medicine.In the prescription,the medicinal properties include both warm and cool,and the medicinal taste is sweet and bitter,which is in line with the compatibility theory of"tastes transforming flavors"in traditional Tibetan medicine prescriptions.

Digital Development Rights in Developing Countries:Where the Governance Rules for Cross-Border Data Flows

The digital development rights in developing countries are based on establishing a new international economic order and ensuring equal participation in the digital globalization process to achieve people's well-rounded development in the digital society.The relationship between cross-border data flows and the realization of digital development rights in developing countries is quite complex.Currently,developing countries seek to safeguard their existing digital interests through unilateral regulation to protect data sovereignty and multilateral regulation for cross-border data cooperation.However,developing countries still have to face internal conflicts between national digital development rights and individual and corporate digital development rights during the process of realizing digital development rights.They also encounter external contradictions such as developed countries interfering with developing countries'data sovereignty,developed countries squeezing the policy space of developing countries through dominant rules,and developing countries having conflicts between domestic and international rules.This article argues that balancing openness and security on digital trade platforms is the optimal solution for developing countries to realize their digital development rights.The establishment of WTO digital trade rules should inherently reflect the fundamental demands of developing countries in cross-border data flows.At the same time,given China's dual role as a digital powerhouse and a developing country,it should actively promote the realization of digital development rights in developing countries.

Promoting grain production through high-standard farmland construction:Evidence in China

Food security is a strategic priority for a country’s economic development.In China,high-standard farmland construction(HSFC)is an important initiative to stabilize grain production and increase grain production capacity.Based on panel data from 31 sample provinces,autonomous regions,and municipalities in China from 2005–2017,this study explored the impact of HSFC on grain yield using the difference-in-differences(DID)method.The results showed that HSFC significantly increased total grain production,which is robust to various checks.HSFC increased grain yield through three potential mechanisms.First,it could increase the grain replanting index.Second,it could effectively reduce yield loss due to droughts and floods.Last,HSFC could strengthen the cultivated land by renovating the low-and medium-yielding fields.Heterogeneity analysis found that the HSFC farmland showed a significant increase in grain yield only in the main grain-producing areas and balanced areas.In addition,HSFC significantly increased the yields of rice,wheat,and maize while leading to a reduction in soybean yields.The findings suggest the government should continue to promote HSFC,improve construction standards,and strictly control the“non-agriculturalization”and“non-coordination”of farmland to increase grain production further.At the same time,market mechanisms should be used to incentivize soybean farming,improve returns and stabilize soybean yields.

THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

Association rule learning(ARL)is a widely used technique for discovering relationships within datasets.However,it often generates excessive irrelevant or ambiguous rules.Therefore,post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors.Recently,several post-processing methods have been proposed,each with its own strengths and weaknesses.In this paper,we propose THAPE(Tunable Hybrid Associative Predictive Engine),which combines descriptive and predictive techniques.By leveraging both techniques,our aim is to enhance the quality of analyzing generated rules.This includes removing irrelevant or redundant rules,uncovering interesting and useful rules,exploring hidden association rules that may affect other factors,and providing backtracking ability for a given product.The proposed approach offers a tailored method that suits specific goals for retailers,enabling them to gain a better understanding of customer behavior based on factual transactions in the target market.We applied THAPE to a real dataset as a case study in this paper to demonstrate its effectiveness.Through this application,we successfully mined a concise set of highly interesting and useful association rules.Out of the 11,265 rules generated,we identified 125 rules that are particularly relevant to the business context.These identified rules significantly improve the interpretability and usefulness of association rules for decision-making purposes.