Rule Algorithm Research Articles

Egalitarian random assignment

Abstract I argue that envy-freeness can obstruct fairness in the random assignment problem and I propose a new fairness criterion called even-handedness. Loosely speaking, a random assignment that maximises the position of the least advantaged agent is even-handed. Rules of random assignment that are stochastic-dominance efficient cannot be both even-handed and envy-free for groups of four or more. I define new rules called positive equality, prudent equality and balanced equality that are even-handed and stochastic-dominance efficient. The positive equality rule is envy-free for groups of three and average-envy-free for groups of any size. I present a general method of extending rules from the domain of strict preference to that of weak preference. That method is applicable to the equality rules, the serial rule and others. I also define a network flow algorithm for the positive equality rule.

Knowledge Integration and Analysis of Technological Innovation in Prefabricated Construction

Prefabricated construction (PC) plays a critical role in advancing the sustainable and high-quality transformation of the construction industry. Nevertheless, the fragmented and variable nature of technological innovations in PC complicates their acquisition, comprehension, and practical application, thereby hindering the process of innovation transformation. In response to these challenges, this study applies knowledge graph techniques to aggregate, correlate, and store knowledge pertaining to PC technological innovations. Specifically, using patent data from the past five years, and grounded in knowledge management and complex network theories, this study employs text mining, topic modeling, and association rule algorithms to perform clustering, evolutionary, and association analyses. The extracted entities and relationships obtained from the analyses are then stored in a Neo4j graph database for the construction and interactive visualization of a knowledge graph for PC technological innovation. According to the knowledge graph, a question-and-answer system framework is further proposed, providing practical application guidance. This research provides a comprehensive overview of the technological landscape, key nodes, and development trends in PC. It makes a meaningful contribution to knowledge management theory and complex network theory, advancing innovative applications in PC technology.

An Association Rule Mining-Based Modeling Framework for Characterizing Urban Road Traffic Accidents

The World Health Organization has recognized road traffic accidents as a global crisis, particularly in urban environments. Despite extensive research endeavors, significant gaps remain in our understanding of how various factors interact to influence urban road traffic incidents. This study analyzed data from 4285 urban road traffic accidents in Hubei Province, employing a two-step clustering algorithm to classify accidents into distinct groups based on specific conditions. Subsequently, association rule mining was utilized to discern relationships between accident characteristics within each cluster. Additionally, a classification based on the association rule algorithm was implemented to develop a predictive model for analyzing factors contributing to casualties. The data were categorized into clusters based on weather and road conditions, with separate discussions conducted for each scenario. The findings indicated that urban congestion is the most critical factor contributing to accidents. Interestingly, rather than in severe weather, accidents were more prevalent during cloudy, light-rain conditions. Electric vehicles and motorcycles emerged as the most vulnerable groups. Furthermore, a notable interaction was observed between the day of the week, time of day, and weather conditions. The predictive model achieved an impressive average accuracy of 86.9%. This methodology facilitates the identification of contributing factors and mechanisms underlying urban road traffic accidents in China and holds potential for establishing accident analysis models in similar contexts. The interactive visualization of association rules further enhances the applicability of the findings. The findings of this study can provide valuable insights for traffic management authorities to understand the causes of urban road traffic accidents, assisting them in devising effective policy measures and countermeasures to reduce the occurrence of accidents and casualties.

Medication Rules of Hub Herb Pairs for Insomnia and Mechanism of Action: Results of Data Mining, Network Pharmacology, and Molecular Docking.

To explore the medication rules of traditional Chinese medicine (TCM) and mechanism of action of hub herb pairs for treating insomnia. Totally 104 prescriptions were statistically analyzed. The association rule algorithm was applied to mine the hub herb pairs. Network pharmacology was utilized to analyze the mechanism of the hub herb pairs, while molecular docking was applied to simulate the interaction between receptors and herb molecules, thereby predicting their binding affinities. The most frequently used herbs in TCM prescriptions for treating insomnia included Semen Ziziphi Spinosae, Radix Glycyrrhizae, Radix et Rhizoma Ginseng, and Poria cum Radix Pini. Among them, the most commonly used were the supplementing herbs, followed by heat-clearing, mind-calming, and exterior-releasing ones, with their properties of warm and cold, flavors of sweet, Pungent, and bitter, and meridian tropisms of liver, lungs, spleen, kidneys, heart, and stomach. The hub herb pairs based on the association rules included Radix Astragali-Radix et Rhizoma Ginseng, Rhizoma Chuanxiong-Radix Glycyrrhizae, Seman Platycladi-Semen Ziziphi Spinosae, Pericarpium Citri Reticulatae-Radix Glycyrrhizae, Radix Polygalae-Semen Ziziphi Spinosae, and Radix Astragali-Semen Ziziphi Spinosae. Network pharmacology revealed that the cAMP signaling pathway might play a key role in treating insomnia synergistically with HIF-1 signaling pathway, prolactin signaling pathway, chemical carcinogenesis receptor activation, and PI3K-Akt signaling pathway. Molecular docking indicated that there was good binding between the active ingredients of the hub herb pairs and the hub targets. This study identified six hub herb pairs for treating insomnia in TCM. These hub herb pairs may synergistically treat insomnia with HIF-1 signaling pathway, prolactin signaling pathway, chemical carcinogenesis receptor activation, and PI3K-Akt signaling pathway through the cAMP signaling pathway.

Music Sequence Creation Technique in Piano Music Production Based on Embedded Neural Network

With the rapid development of computer technology and artificial intelligence, especially the rise of embedded neural network technology. The traditional arrangement methods are facing increasing challenges in the field of music production, and their complexity and time-consuming nature are no longer sufficient to meet the urgent needs of modern audiences for efficient and high-quality computer piano music works. This paper proposes an innovative solution — a piano music production matching method based on embedded neural network fusion, and particularly focused on intelligent creation technology for music sequences. In the system, embedded neural networks are core applied in the process of creating music sequences. By utilizing their powerful feature learning and generalization abilities, neural networks can capture complex patterns and dynamic changes in music sequences, providing infinite possibilities for melody generation and innovation. At the same time, combined with association rule algorithms, the system can discover potential association patterns between notes, further optimizing the fluency and harmony of the melody; The clustering algorithm helps extract common features from massive music data, providing a rich material library for melody innovation.

Enhancing employee performance appraisal through optimized association rule algorithms: a data mining approach

The planning and development department of the China Yangtze river three gorges engineering development corporation aims to improve its intelligent performance management system in accordance with the company’s vision. This paper presents a study on enhancing intelligent performance management in the Planning and Development Department of the China Yangtze River Three Gorges Engineering Development Corporation. By combining data mining techniques with association rule algorithms, a performance prediction model was established. The goal is to explore a performance management system aligned with the company’s vision and the specific characteristics of construction enterprises. Experimental results show the effectiveness of the GABPNN model, achieving a prediction error of 5% or less in seven out of seventeen sample points, with a maximum relative error of 20.86%. The suggested performance management system presents a useful strategy for construction companies to improve their performance evaluation procedures, boosting overall management efficiency and aligning with their distinctive traits.

Assessment of land restoration from optical satellite image time-series

Abstract. Ecosystem conservation and restoration is recognized by the international community as a key strategy for human well-being as well as our planet’s health. Assessing the efficiency of actions implemented in this context is essential, as their application on the ground may prove challenging. In the present paper, we present a methodology for characterising vegetation regeneration. This methodology has been developed and tested at the scale of the Sao Paulo state. It is based on two successive steps. Firstly, annual land cover maps are produced with breakpoints detection and Random Forest classification. This process is performed on biophysical variables derived from more than 10 000 optical images (2016 – 2021 included) and 4774 reference data. Secondly, we applied an expert-based rules algorithm to derive land regeneration map from annual land cover time series. The distinction is made between natural and anthropogenic regeneration. The accuracy assessment shows an overall accuracy of more than 80% for both the annual land cover maps and the regeneration map.

Association Rules Analysis for Continuous Chicken Egg Traits Dataset

This study aims to apply the Apriori association rule algorithm on 14 continuous egg quality traits recorded from 4320 eggs of three commercial white-laying chicken lines. In the study all the continuous data were discretized using Equal-Width-Interval method based the number of intervals obtained with Rice formula. Association rules analysis on the discretized dataset resulted with a total of 349 rules consists of 3 and 4 items. According to the top five rules by support and confidence, some important associations were obtained between the certain value ranges of the traits egg weight, egg width, egg length, shell thickness, and shell breaking strength when compared to the others. The appropriate biological and economic interpretations of the obtained rules may contribute to the poultry industry in practice.

Prompt Update Algorithm Based on the Boolean Vector Inner Product and Ant Colony Algorithm for Fast Target Type Recognition

In recent years, data mining technology has become increasingly popular, evolving into an independent discipline as research deepens. This study constructs and optimizes an association rule algorithm based on the Boolean vector (BV) inner product and ant colony optimization to enhance data mining efficiency. Frequent itemsets are extracted from the database by establishing BV and performing vector inner product operations. These frequent itemsets form the problem space for the ant colony algorithm, which generates the maximum frequent itemset. Initially, data from the total scores of players during the 2022–2024 regular season was analyzed to obtain the optimal lineup. The results obtained from the Apriori algorithm (AA) were used as a standard for comparison with the Confidence-Debiased Adversarial Fuzzy Apriori Method (CDAFAM), the AA based on deep learning (DL), and the proposed algorithm regarding their results and required time. A dataset of disease symptoms was then used to determine diseases based on symptoms, comparing accuracy and time against the original database as a standard. Finally, simulations were conducted using five batches of radar data from the observation platform to compare the time and accuracy of the four algorithms. The results indicate that both the proposed algorithm and the AA based on DL achieve approximately 10% higher accuracy compared with the traditional AA. Additionally, the proposed algorithm requires only about 25% of the time needed by the traditional AA and the AA based on DL for target recognition. Although the CDAFAM has a similar processing time to the proposed algorithm, its accuracy is lower. These findings demonstrate that the proposed algorithm significantly improves the accuracy and speed of target recognition.

Asymptotic synchronisation for coupled heterogeneous complex networks via L' Hoptial' rule

This paper considers the global asymptotic synchronisation (GAS) for the drive-response coupled heterogeneous complex networks (DRCHCNS). By using the different inequality techniques (DIT), L' Hoptial' rule and limit algorithms, three criteria to assure the GAS for the DRCHCNS are derived. Up to now, the results on GAS for DRCHCNS have been obtained usually by applying linear matrix inequality (LMI) and matrix measure approach (MMA). But, in this paper, without applying above approaches, we establish the GAS criteria for the DRCHCNS by using L' Hoptial' rule and limit algorithms. So, our study approach for the complex networks (CNS) extends the study fields of synchronisation of the CNS.

Path analysis of coal mine accident risk factors based on the 24Model.

Coal mine accidents seriously affect China's coal safety production and sustainable development. The present study aimed to reveal the risk factors in coal mine accidents and explore the causal relationship among risk factors. This study utilized text mining to analyse 450 coal mine accident reports, identifying 50 risk factors and efficiently mapping them into the 24Model. The association rule algorithm was then used to mine the strong association rules among the risk factors within the 24Model, establishing the interaction mechanism among them. Based on the strong association rules, related hypotheses were proposed. Finally, the hierarchical and logical relationships of risk factors within the 24Model were analysed, and the causal and mediating effects were tested by path analysis. The safety management system has a direct effect on unsafe acts, unsafe conditions, habitual behaviour and organizational safety culture. Moreover, external influence has an effect on unsafe acts, organizational safety culture and habitual behaviour through the mediating effect of the safety management system. Based on the results obtained, this study proposes a series of specific measures to prevent risks in coal mines, providing a new perspective for the analysis and prevention of accidents.

Web log mining techniques to optimize Apriori association rule algorithm in sports data information management

To optimize the current college sports data information management system, this study combines the Apriori association rule algorithm with web application development technology to upgrade the management system. Firstly, this study explores novel log mining techniques in genetic algorithms and web application development technology. Secondly, by integrating log mining techniques to optimize the Apriori algorithm, associations between sports data and information are discovered. Through the optimized algorithm, this study identifies key association rules of sports data information and validates the optimized system’s reliability and effectiveness through experiments. The experimental results show that the running time of the traditional Apriori algorithm exponentially grows with the increase in information volume, while the optimized execution efficiency is improved by approximately 10–15%. Additionally, the average retrieval accuracy of this optimized system can reach 98.3%, although the retrieval time also increased by 23%. Therefore, the technology and algorithms proposed in this study have certain application value in the sports information management system and contribute to the optimization of data information management in this field.

Study on Audit Risk Model Based on Data Mining Algorithm

With the widespread application of modern information technologies such as big data, enterprises have generated a large amount of data in their daily operations. The traditional audit methods have drawbacks such as high cost and high consumption, and can no longer meet the needs of audit work in the digital era. Therefore, it is urgent to adopt audit methods suitable for the digital era to improve audit quality and reduce enterprise Audit risk. For professional auditors in enterprises, it is very important to utilize emerging technologies such as data mining algorithms. The audited enterprise may tamper with its financial statements, and identifying high-quality audit data from massive amounts of data is a huge challenge. Compared to traditional audit methods, data mining algorithms can significantly improve the efficiency and accuracy of audit work. For example, the BP neural network, with its powerful nonlinear mapping ability, can capture complex relationships in data; Support vector machines classify data by finding the optimal hyperplane in high-dimensional space and have good generalization ability; random forest reduces overfitting and improves prediction accuracy by integrating multiple decision trees; Association rule algorithms can discover interesting relationships between data items, helping auditors identify potential fraudulent behavior or abnormal transactions. Therefore, the purpose of this study is to accurately evaluate and reduce the audit risk of enterprises, and to build an audit risk model using computer data mining algorithms. This provides necessary reference and guidance for auditors to conduct data analysis and mine valuable data during the audit process of enterprises, thereby improving audit efficiency.

Aggregation-Decomposition Coupling Drawdown Rule and Progressive Optimal Algorithm for Optimization of Large-Scale Reservoirs

Aggregation-Decomposition Coupling Drawdown Rule and Progressive Optimal Algorithm for Optimization of Large-Scale Reservoirs

Application of association rules and simulated annealing algorithms in optimizing traditional Chinese medicine placement schemes

Background and aimChina has used traditional Chinese medicine (TCM) to treat diseases for more than 2000 years. Traditionally, TCMs in medicine cabinets are arranged alphabetically or on the basis of experience, but this arrangement greatly affects dispensing efficiency. However, owing to the unique properties and qualities of TCM, very few automatic approaches or systems have specifically addressed TCM dispensing problems. Therefore, it is necessary to establish a method of optimizing the traditional Chinese medicine placement scheme (TCMPS) via computer algorithms to improve the work efficiency of pharmacists.MethodsA prescription dataset from a hospital in 2022 was obtained, and the association rule algorithm (ARA) was used to calculate the frequency of use for each type of TCM and the associations between different types of TCMs. On the basis of these association and frequency data, the optimal TCMPS was calculated using the simulated annealing algorithm (SAA) and then verified using the prescription dataset from 2023.ResultsA total of 10,601 prescriptions were collected in 2022, involving 360 different TCMs, and each prescription contained an average of 9.485 TCMs, with Danggui (3628) being the most frequently used. When the threshold of support was set to 0.05 and the confidence was set to 0.8, 78 couplet medicines used in orthopedics clinics were found through ARA. When the threshold value of support was set to 0, the confidence was set to 0, and the rule length was 2, a total of 129,240 rules were obtained, indicating support between all pairwise TCMs. The TCMPS, calculated using SAA, had a correlation sum of 14.183 and a distance sum of 3.292. The TCMPS was verified using a prescription dataset from 2023 and theoretically improved the dispensing efficiency of pharmacists by approximately 50%.ConclusionsIn this study, the ARA and SAA were successfully applied to pharmacies for the first time, and the optimal TCMPS was calculated. This approach not only significantly improves the dispensing efficiency of pharmacists and reduces patient waiting time but also enhances the quality of medical services and patient satisfaction, and provides a valuable reference for the development of smart medicine.

A three-stage prediction model for firm default risk: An integration of text sentiment analysis

Predicting firm default risk is vital for financial institutions to avert significant economic losses, making the enhancement of its prediction precision both imperative and intricate. This research introduces a three-stage prediction model, including association rule algorithm (ARA), support vector machine (SVM) and modified particle swarm optimization algorithm (MPSO). Features selected by ARA are used as inputs for SVM, and penalty parameter and kernel parameter of SVM is optimized by MPSO that uses the adaptive inertia weight. The importance of text sentiment variables are emphasized to predict firm default risk. In the first stage, feature selection seeks to curtail the dimensions of both financial and non-financial variables. The empirical findings validate the efficacy of the ARA, revealing a strong correlation between text sentiment and default risk. The subsequent two stages deploy the SVM, refined by the MPSO, to predict the default risk. Compared with renowned models, the proposed model displays superior prediction precision and a reduced computational overhead. This research furnishes a potent instrument for regulators and firms alike, aiding in mitigating prospective default risks and forestalling broader economic upheavals.

Sustainable scheduling of TFT-LCD cell production: A hybrid dispatching rule and two-phase genetic algorithm

TFT-LCD manufacturing process involves Array, Color Filter (CF), Cell, and Module. The Cell process is pivotal in the TFT-LCD production supply chain; it bridges the front-end (Array and CF) and back-end (Module), maintaining a balance of production capacities. Supply from the front-end is transmitted to the back-end, ensuring prompt responsiveness to customer demands. As awareness of environmental conservation grows, implementing sustainable development in the TFT-LCD manufacturing industry has become crucial. Specifically, reducing the machine setup frequency in TFT-LCD scheduling decreases energy consumption during machine operation and wait times and prolongs equipment lifespan by minimizing wear from repeated shutdowns. To tackle the intricate scheduling challenges within TFT-LCD production, this study proposes an intelligent, sustainable scheduling approach that employs a rolling dispatching and line balancing mechanism and a two-phase genetic algorithm (HDTPGA) for Cell scheduling. The goal is to minimize makespan considering product diversity, production constraints, work-in-process balancing, and machine setup frequency. This holistic approach promotes energy-efficient and sustainable manufacturing practices. According to the empirical study, the proposed HDTPGA reduces the makespan by approximately 10% compared to the baseline. Additionally, it reduces the average daily setup frequency of bottleneck stages (ODF and CCT) in the Cell process by approximately 68.25% and 66.25%, respectively. The main contribution of this paper is to propose an intelligent scheduling algorithm to optimize Cell production scheduling in practice. The HDTPGA achieves equilibrium between front-end and back-end production, utilizing an efficient line change mechanism to reduce energy consumption. The enhancement in efficiency not only restricts material waste but also reduces the environmental impact, enabling manufacturers to adopt green production practices and promoting a transition of the entire panel industry toward sustainability.

State Evaluation of Electrical Equipment in Substations Based on Data Mining

This paper explores the combination of a data mining-based state evaluation method for electrical equipment in substations, analyzing the effectiveness and accuracy. First, a Gaussian mixture model is applied to fit all raw data of electrical equipment. The Expectation Maximization algorithm summarizes the data distribution characteristics and identifies outliers. The a priori algorithm is then employed for data mining to derive frequent itemsets and association rules between equipment quality and measurement data. For new equipment samples, conditional probabilities of each feature are independently calculated and combined to classify and evaluate equipment quality. The results suggest that equipment reliability in smart substations can be inferred from historical and real-time operational data using improved association rule algorithms and Naive Bayes classifiers. Finally, the proposed method was applied to analyze statistical data from a 110 kV substation of a power supply company. The states prediction accuracy exceeded 95% when compared with actual equipment quality. The effectiveness evaluation metrics demonstrated that this method outperforms single-category algorithms in terms of accuracy and discrimination ability.

DNN-Based Resource Recommendation for Ideology Theory Courses Online

Revamped IPE confronts static material constraints and outdated pedagogy, warranting integration of web resources and big data analytics for instructional innovation. Digital IPE adoption in vocational education optimizes online resource use, enhancing teaching effectiveness. Introducing CUPMF, a personalized learning model, we conduct empirical assessments on a large dataset (364,617+ entries) from Smart Classroom's cloud platform and public datasets, reflecting varied IPE scenarios. Comparative experiments against association rule, content-, tag-based, and collaborative filtering algorithms show CUPMF's superiority. It achieves a 11.61% F1 score boost over four alternatives for basic recommendations and outperforms Que Rec by 1.975%. Complexity-wise, CUPMF registers an 11.52% mean F1 score increment over four methods and 1.875% over Que Rec. Proven, CUPMF markedly improves IPE resource recommendation accuracy and efficacy, poised to transform personalized online vocational learning.

English Network Teaching Model and Design of Evaluation System Based on Association Rule Algorithm

This study innovates English network teaching by applying a refined Association Rule Mining (ARM) algorithm. It integrates an “interest” parameter into ARM, dynamically adapting content to individual learners' profiles, improving engagement and outcomes. Controlled experiments, spanning diverse online platforms, validate the ARM model's efficacy by correlating learning content with academic performance, specifically CET-4 and CET-6 scores. Comprehensive preprocessing ensures data quality and privacy, employing techniques like de-identification, data perturbation, and aggregation. Advanced data analysis, including cross-validation and multivariate techniques, bolsters findings' reliability. Results highlight the ARM model's capacity to generate personalized learning paths, transcending conventional methods, and its potential as a cornerstone for data-driven education reforms. Future research will explore machine learning refinements and cultural adaptability to broaden its impact, fostering equitable, high-quality digital English education worldwide.