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.

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.

Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining

Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database. By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns. We modified Grover’s search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space. We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold. The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace. Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance. Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits.

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).

STRONGLY CONVERGENT INERTIAL FORWARD-BACKWARD-FORWARD ALGORITHM WITHOUT ON-LINE RULE FOR VARIATIONAL INEQUALITIES

This paper studies a strongly convergent inertial forward-backward-forward algorithm for the variational inequality problem in Hilbert spaces.In our convergence analysis,we do not assume the on-line rule of the inertial parameters and the iterates,which have been assumed by several authors whenever a strongly convergent algorithm with an inertial extrapolation step is proposed for a variational inequality problem.Consequently,our proof arguments are different from what is obtainable in the relevant literature.Finally,we give numerical tests to confirm the theoretical analysis and show that our proposed algorithm is superior to related ones in the literature.

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.

Strengthening Active Aging through Older People’s Association and Economic Activity of the Older People in Nepal

Aging is a natural lifelong process ending in death. Many older people are living in poverty. Older people are generally considered dependent on others as they grow older. The purpose of this article is to explore the entrepreneurship activities of Nepalese older adults. Data for this study were collected from the project Help Age International (HAI) implemented in Nepal. Qualitative data observations and interviews were used to collect data. The findings of this study show the formation of the Older People’s Association (OPA) has supported many older people to participate outside the home in various social activities. Moreover, regular deposits through OPAs offer little help. OPAs support older people in their need of financial support to implement minor entrepreneurship. Older people who received support were pleased and were actively involved in their activities and also regularly deposited money in them. Subsequently, older people’s participation in social activities has increased and also helped to lower elderly abuse, loneliness, and depression. Local governments should promote such activities which will help with healthy aging.

Rule acquisition of three-way semi-concept lattices in formal decision context

Three-way concept analysis is an important tool for information processing,and rule acquisition is one of the research hotspots of three-way concept analysis.However,compared with three-way concept lattices,three-way semi-concept lattices have three-way operators with weaker constraints,which can generate more concepts.In this article,the problem of rule acquisition for three-way semi-concept lattices is discussed in general.The authors construct the finer relation of three-way semi-concept lattices,and propose a method of rule acquisition for three-way semi-concept lattices.The authors also discuss the set of decision rules and the relationships of decision rules among object-induced three-way semi-concept lattices,object-induced three-way concept lattices,classical concept lattices and semi-concept lattices.Finally,examples are provided to illustrate the validity of our conclusions.

Advancements in Medication Rule for Pulmonary Nodules: A Review of Current Research Progress

This paper reviewed the literature on medication rule of pulmonary nodules in recent years. It is found that contemporary doctors pay more attention to regulating Qi, clearing heat and detoxifying, eliminating phlegm, dissolving phlegm and dissipating masses. They use mild drugs, cold and warm treatments in parallel, combining the tastes of pungent, bitterness, and sweetness at the same time. The treatment focuses on the five viscera with emphasis on the lung meridian while also considering the spleen and stomach functions as well as soothing liver stagnation. This information aims to provide some reference for clinical treatment of pulmonary nodules.

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.

Composite Fractional Trapezoidal Rule with Romberg Integration

The aim of this research is to demonstrate a novel scheme for approximating the Riemann-Liouville fractional integral operator.This would be achieved by first establishing a fractional-order version of the 2-point Trapezoidal rule and then by proposing another fractional-order version of the(n+1)-composite Trapezoidal rule.In particular,the so-called divided-difference formula is typically employed to derive the 2-point Trapezoidal rule,which has accordingly been used to derive a more accurate fractional-order formula called the(n+1)-composite Trapezoidal rule.Additionally,in order to increase the accuracy of the proposed approximations by reducing the true errors,we incorporate the so-called Romberg integration,which is an extrapolation formula of the Trapezoidal rule for integration,into our proposed approaches.Several numerical examples are provided and compared with a modern definition of the Riemann-Liouville fractional integral operator to illustrate the efficacy of our scheme.

Leveraging the potential of big genomic and phenotypic data for genome-wide association mapping in wheat

Genome-wide association mapping studies(GWAS)based on Big Data are a potential approach to improve marker-assisted selection in plant breeding.The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing.Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects,but is hindered by data heterogeneity and lack of interoperability.In this study,we used genomic and phenotypic data sets,focusing on Central European winter wheat populations evaluated for heading date.We explored strategies for integrating these data and subsequently the resulting potential for GWAS.Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols,resulting in high quality integrated phenotypic data.In this context,genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series,which was low in our case.Contrary to expectations,fewer associations between markers and traits were found in the larger combined data than in the individual experimental series.However,the predictive power based on the marker-trait associations of the integrated data set was higher across data sets.Therefore,the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS.The results encourage further efforts to standardize and share data in the plant breeding community.

Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.

Genome-Wide Association Study of Cooked Rice Textural Attributes and Starch Physicochemical Properties in indica Rice

Rice cooking and eating qualities(CEQ)are mainly determined by cooked rice textural parameters and starch physicochemical properties.However,the genetic bases of grain texture and starch properties in rice have not been fully understood.We conducted a genome-wide association study for apparent amylose content(AAC),starch pasting viscosities,and cooked rice textural parameters using 279 indica rice accessions from the 3000 Rice Genome Project.We identified 26 QTLs in the whole population and detected single nucleotide polymorphisms(SNPs)with the lowest P-value at the Waxy(Wx)locus for all traits except pasting temperature and cohesiveness.Additionally,we detected significant SNPs at the SUBSTANDARD STARCH GRAIN6(SSG6)locus for AAC,setback(SB),hardness,adhesiveness,chewiness(CHEW),gumminess(GUM),and resilience.We subsequently divided the population using a SNP adjacent to the Waxy locus,and identified 23 QTLs and 12 QTLs in two sub-panels,WxT and WxA,respectively.In these sub-panels,SSG6 was also identified to be associated with pasting parameters,including peak viscosity,hot paste viscosity,cold paste viscosity,and consistency viscosity.Furthermore,a candidate gene encoding monosaccharide transporter 5(OsMST5)was identified to be associated with AAC,breakdown,SB,CHEW,and GUM.In total,39 QTLs were co-localized with known genes or previously reported QTLs.These identified genes and QTLs provide valuable information for genetic manipulation to improve rice CEQ.

Automatic Rule Discovery for Data Transformation Using Fusion of Diversified Feature Formats

This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost,a machine learning algorithm renowned for its efficiency and performance.The framework proposed herein utilizes the fusion of diversified feature formats,specifically,metadata,textual,and pattern features.The goal is to enhance the system’s ability to discern and generalize transformation rules fromsource to destination formats in varied contexts.Firstly,the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the model.Subsequent sections expound on the mechanism of feature optimization using Recursive Feature Elimination(RFE)with linear regression,aiming to retain the most contributive features and eliminate redundant or less significant ones.The core of the research revolves around the deployment of the XGBoostmodel for training,using the prepared and optimized feature sets.The article presents a detailed overview of the mathematical model and algorithmic steps behind this procedure.Finally,the process of rule discovery(prediction phase)by the trained XGBoost model is explained,underscoring its role in real-time,automated data transformations.By employingmachine learning and particularly,the XGBoost model in the context of Business Rule Engine(BRE)data transformation,the article underscores a paradigm shift towardsmore scalable,efficient,and less human-dependent data transformation systems.This research opens doors for further exploration into automated rule discovery systems and their applications in various sectors.

Genome-wide association study of seedling nitrogen-use efficiency-associated traits in common wheat(Triticum aestivum L.)

Nitrogen(N)fertilizer application is essential for crop-plant growth and development.Identifying genetic loci associated with N-use efficiency(NUE)could increase wheat yields and reduce environmental pollution caused by overfertilization.We subjected a panel of 389 wheat accessions to N and chlorate(a nitrate analog)treatments to identify quantitative trait loci(QTL)controlling NUE-associated traits at the wheat seedling stage.Genotyping the panel with a 660K single-nucleotide polymorphism(SNP)array,we identified 397 SNPs associated with N-sensitivity index and chlorate inhibition rate.These SNPs were merged into 49 QTL,of which eight were multi-environment stable QTL and 27 were located near previously reported QTL.A set of 135 candidate genes near the 49 QTL included TaBOX(F-box family protein)and TaERF(ethylene-responsive transcription factor).A Tabox mutant was more sensitive to low-N stress than the wild-type plant.We developed two functional markers for Hap 1,the favorable allele of TaBOX.

Learning-based user association and dynamic resource allocation in multi-connectivity enabled unmanned aerial vehicle networks

Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in B5G.Besides,dynamic resource allocation and multi-connectivity can be adopted to further harness the potentials of UAVs in improving communication capacity,in such situations such that the interference among users becomes a pivotal disincentive requiring effective solutions.To this end,we investigate the Joint UAV-User Association,Channel Allocation,and transmission Power Control(J-UACAPC)problem in a multi-connectivity-enabled UAV network with constrained backhaul links,where each UAV can determine the reusable channels and transmission power to serve the selected ground users.The goal was to mitigate co-channel interference while maximizing long-term system utility.The problem was modeled as a cooperative stochastic game with hybrid discrete-continuous action space.A Multi-Agent Hybrid Deep Reinforcement Learning(MAHDRL)algorithm was proposed to address this problem.Extensive simulation results demonstrated the effectiveness of the proposed algorithm and showed that it has a higher system utility than the baseline methods.

Casual associations between blood metabolites and colon cancer

BACKGROUND Limited knowledge exists regarding the casual associations linking blood metabolites and the risk of developing colorectal cancer.AIM To investigate causal associations between blood metabolites and colon cancer.METHODS The study utilized a two-sample Mendelian randomization(MR)analysis to investigate the causal impact of 486 blood metabolites on colorectal cancer.The primary method of analysis used was the inverse variance weighted model.To further validate the results several sensitivity analyses were performed,including Cochran's Q test,MR-Egger intercept test,and MR robust adjusted profile score.These additional analyses were conducted to ensure the reliability and robustness of the findings.RESULTS After rigorous selection for genetic variation,486 blood metabolites were included in the MR analysis.We found Mannose[odds ratio(OR)=2.09(1.10-3.97),P=0.024],N-acetylglycine[OR=3.14(1.78-5.53),P=7.54×10^(-8)],X-11593-O-methylascorbate[OR=1.68(1.04-2.72),P=0.034],1-arachidonoylglycerophosphocholine[OR=4.23(2.51-7.12),P=6.35×10^(-8)]and 1-arachidonoylglycerophosphoethanolamine 4[OR=3.99(1.17-13.54),P=0.027]were positively causally associated with colorectal cancer,and we also found a negative causal relationship between Tyrosine[OR=0.08(0.01-0.63),P=0.014],Urate[OR=0.25(0.10-0.62),P=0.003],N-acetylglycine[0.73(0.54-0.98),P=0.033],X-12092[OR=0.89(0.81-0.99),P=0.028],Succinylcarnitine[OR=0.48(0.27-0.84),P=0.09]with colorectal cancer.A series of sensitivity analyses were performed to confirm the rigidity of the results.CONCLUSION This study showed a causal relationship between 10 blood metabolites and colorectal cancer,of which 5 blood metabolites were found to be causal for the development of colorectal cancer and were confirmed as risk factors.The other five blood metabolites are protective factors.

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.