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Large-scale manufacturers aim to reduce the number of faulty products by finding tools or process factors that cause product faults through tool monitoring. The causes of faulty electronic components such as semiconductor chips and printed circuit boards (PCBs) include abnormalities in single tools and abnormalities caused by interactions between the tools of a specific process and a related process. Here, the tools exhibiting an interaction effect are called the fault-introducing tool group. This study presents a numerical association rule mining method for discovering the fault-introducing tool groups based on a genetic algorithm. A novel fitness function and rule pruning process are developed to identify the fault-introducing tool groups. The effectiveness of the method is verified using simulations and a case study of actual PCB production lines. The proposed method can discover fault-introducing tool groups better than machine learning algorithms. Additionally, the method can accurately identify fault-introducing tool groups in various manufacturing environments, such as those with highly skewed yield distributions or variations in yield distributions over time. In an actual PCB production line, the groups identified by the proposed method produced up to 36.5% more faulty chips than those identified by the comparison models. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research is to determine the pattern of access to information resources and analysis of loan transactions among users of public libraries in Ilam province using the approach of association rules, clustering and classification in data mining. Method: In this research, by using data mining techniques, and algorithms of convergence, clustering, and classification rules on data related to the circulation desk of public libraries in Ilam province, the demographic information affecting the study behavior of members and the patterns of using information resources by this heterogeneous community were extracted. The data mining process used in the research (CRISP-DM) consists of six pahses, including business understanding, data understanding, data preparation, modeling (rules of convergence, artificial neural network, and decision tree), evaluation, and deployment, respectively. Findings: The findings related to different groups using information sources showed that in addition to studying their textbooks, students also studied subjects such as Iranian history and Islamic religion. In terms of gender, women with bachelorʼs degrees were interested in studying Persian language and literature, and fiction. The analysis of resource lending in terms of gender showed that 58 percent of active members of public libraries in Ilam province were women and 42 percent of them were men. This difference shows that women have a greater desire to use the information resources of public libraries in Ilam province. Furthermore, among those who were late in returning their borrowed books, 54 percent were women and 46 percent were men; In terms of returning books on time, women showed their responsibility with 52 percent and men with 48 percent. The results show that men with diplomas were interested in issues related to Islam and this group felt more responsibility for returning their books. In terms of gender and its effect on the studied subjects, undergraduate women, who had free jobs, showed interest in the classes of Persian literature and fiction and did not delay in returning their books. The analysis of the behavior model of the patrons in terms of book return revealed that the men with diploma who had free jobs did not delay in returning loaned books, and the women with diploma and bachelorʼs degrees who had free jobs did not delay in returning loaned books. Moreover, students with undergraduate degrees delayed in returning loaned books. A neural network model was developed to predict the delay in the return of books based on characteristics such as gender and educational level, and the accuracy rate of the model during the learning process with the best prediction accuracy was equal to 69.18%. This shows that it is possible to learn and predict the output by receiving new entries in the database of public libraries of Ilam province. Originality/value: The deployment of the proposed neural network model on the database of public libraries of Ilam province for predicting the delay in the return of books based on different characteristics, shows that it is possible to learn and predict the output by receiving new entries in the database of these libraries. Moreover, using the association rules technique, it is possible to design a book recommendation system in public libraries of Ilam province. [ABSTRACT FROM AUTHOR]

Against the backdrop of the rapid development of information technology, the total amount of data has exploded, and efficient association rule mining methods for large-scale datasets have been studied. Conventional rule mining algorithms are subject to electrical constraints when working, and their convergence speed and data noise are currently the main problems they face. In order to accelerate the working process of the algorithm, this study introduces a data warehouse into the K-Means algorithm. The time series and voltage interaction functions are connected with the long-and-short-term memory network for efficient information analysis of power grid data, generating fusion algorithms. When mining association rules in electrical data sets, the pruning strategy is used to effectively reduce the search space, thus improving the efficiency of the algorithm. The pruning strategy applied in this study is confidence pruning, and the rules that do not meet the conditions are deleted by calculating confidence. For large-scale data sets, the distributed computing framework can dispatch tasks to multiple computing nodes, thus accelerating the computing speed. In the voltage interaction experiment, the storage and processing cost of electrical data is very high, so the research on using compression technology to reduce the dimension of storage space will reduce the computational complexity of the algorithm. The study conducts experiments on the Netloss dataset and three models, including long-and-short-term memory networks, to verify the superiority of the fusion algorithm. Under the same experimental voltage, the circuit power flows of the four models were 0.37, 0.64, 0.79, and 0.82A, respectively, indicating that the algorithm effectively controlled the electrical dataset. Its measurement accuracy was the highest among the four models, at 91.7%. The experimental results showed that the fusion algorithm proposed in the study had precise control ability in power grid datasets, and effectively mined association rules on large-scale datasets. This paper proposes a novel approach for efficient association rule mining in large-scale power grid datasets. A fusion algorithm is developed combining clustering, neural networks, and association rule learning. The technique incorporates data warehousing, time series modeling, and voltage interaction analysis to enhance information extraction from electrical data. Experiments on the Netloss dataset demonstrate the algorithm's effectiveness for power flow control and measurement accuracy compared to standard methods. Results show significant improvements in active power loss and network loss metrics as well. This research provides an important foundation for scalable analytics in smart power systems. [ABSTRACT FROM AUTHOR]

MiRNA (microRNA)‐disease association prediction has essential applications for early disease screening. The process of traditional biological experimental validation is both time‐consuming and expensive. However, as artificial intelligence technology continues to advance, computational methods have become efficient tools for predicting miRNA‐disease associations. These methods often rely on the combination of multiple sources of association data and require improved feature mining. This study proposes a dynamic graph attention‐based association prediction model, DGAMDA, which combines feature mapping and dynamic graph attention mechanisms through feature mining on a single miRNA‐disease association network. DGAMDA effectively solves the problems of feature heterogeneity and inadequate feature mining by previous static graph attention mechanisms and achieves high‐precision feature mining and association scoring prediction. We conducted a five‐fold cross‐validation experiment and obtained the mean values of Accuracy, Precision, Recall, and F1‐score, which were.8986,.8869,.9115, and.8984, respectively. Our proposed model outperforms other advanced models in terms of experimental results, demonstrating its effectiveness in feature mining and association prediction based on a single association network. In addition, our model can also be used to predict miRNAs associated with unknown diseases. [ABSTRACT FROM AUTHOR]

Being motivated has positive influences on task performance. However, motivation could result from various motives that affect different parts of the brain. Analyzing the motivation effect from all affected areas requires a high number of EEG electrodes, resulting in high cost, inflexibility, and burden to users. In various real-world applications, only the motivation effect is required for performance evaluation regardless of the motive. Analyzing the relationships between the motivation-affected brain areas associated with the task's performance could limit the required electrodes. This study introduced a method to identify the cognitive motivation effect with a reduced number of EEG electrodes. The temporal association rule mining (TARM) concept was used to analyze the relationships between attention and memorization brain areas under the effect of motivation from the cognitive motivation task. For accuracy improvement, the artificial bee colony (ABC) algorithm was applied with the central limit theorem (CLT) concept to optimize the TARM parameters. From the results, our method can identify the motivation effect with only FCz and P3 electrodes, with 74.5% classification accuracy on average with individual tests. [ABSTRACT FROM AUTHOR]

Objective: Whether coal mine dust exposure increases cardiovascular diseases (CVDs) risk was rarely explored. Our objective was to examine the association between coal mine dust exposure and cardiovascular risk. Methods: We estimated cumulative coal mine dust exposure (CDE) for 1327 coal miners by combining data on workplace dust concentrations and work history. We used brachial-ankle pulse wave velocity (baPWV, a representative indicator of arterial stiffness) and ten-year atherosclerotic cardiovascular disease (ASCVD) risk to assess potential CVD risk, exploring their associations with CDE. Results: Positive dose-response relationships of CDE with baPWV and ten-year ASCVD risk were observed after adjusting for covariates. Specifically, each 1 standard deviation (SD) increase in CDE was related to a 0.27 m/s (95% CI: 0.21, 0.34) increase in baPWV and a 1.29 (95% CI: 1.14, 1.46) elevation in OR (odds ratio) of risk of abnormal baPWV. Moreover, each 1 SD increase in CDE was associated with a 0.74% (95% CI: 0.63%, 0.85%) increase in scores of ten-year ASCVD and a 1.91 (95% CI: 1.62, 2.26) increase in OR of risk of ten-year ASCVD. When compared with groups unexposed to coal mine dust, significant increase in the risk of arterial stiffness and ten-year ASCVD in the highest CDE groups were detected. Conclusion: The study suggested that cumulative exposure to coal mine dust was associated with elevated arterial stiffness and ten-year ASCVD risk in a dose-response manner. These findings contribute valuable insights for cardiovascular risk associated with coal mine dust. [ABSTRACT FROM AUTHOR]

Differential item functioning (DIF) is a common challenge when examining latent traits in large scale surveys. In recent work, methods from the field of machine learning such as model-based recursive partitioning have been proposed to identify subgroups with DIF when little theoretical guidance and many potential subgroups are available. On this basis, we propose and compare recursive partitioning techniques for detecting DIF with a focus on measurement models with multiple latent variables and ordinal response data. We implement tree-based approaches for identifying subgroups that contribute to DIF in multidimensional latent variable modeling and propose a robust, yet scalable extension, inspired by random forests. The proposed techniques are applied and compared with simulations. We show that the proposed methods are able to efficiently detect DIF and allow to extract decision rules that lead to subgroups with well fitting models. [ABSTRACT FROM AUTHOR]

Improving the sales of e-commerce platforms is the primary goal of this paper. This paper studies the data of e-commerce product recommendations from the perspective of user preference and association rules. The characteristics of positive and reverse association rules in data mining are analyzed. Then, a multi-dimension association rule calculation method is proposed. Create a data attribute unit set. By analyzing each attribute's weighted coefficient and similarity, the attribute confidence degree is obtained, and the data is preprocessed. An example is given to verify the effectiveness of the proposed method. The recommendation engine based on user preferences and association rules significantly improves the accuracy, recall rate and prediction coverage of e-commerce recommendation systems. [ABSTRACT FROM AUTHOR]

Mining for natural resources on-Earth is commonplace and dates back over a hundred years at an industrial scale. Technological advances in outer space exploration are enabling the mining of extraterrestrial resources to transition from mere science fiction, to a serious possibility. In recent decades, several new start-up companies have arisen with the sole intention of exploiting resources that exist in outer space, such as on Earth's moon, asteroids, meteorites, planets, and various planetary satellites, such as the moons of Mars - Phobos and Diemos. However, despite the increased investment and interest in space mining, criminologists have remained virtually silent on outer space issues. In this paper we adopt a green criminological approach to explain the emergence of outer space mining, and argue that now is the time to be researching and debating the phenomenon of extraterrestrial mining in order to prevent future social and environmental harm (following the precautionary principle of environmental law). To do this, the paper does three things. Firstly, it examines strategies for conducting space mining (such as its feasibility, probable locations, and innovative mining techniques). Secondly, it analyses the terrestrial and extraterrestrial impacts of space mining, unveiling several avenues for the creation of social and environmental harm. Finally, it uses a green criminological approach to justify the rationale for engaging legal scholars and criminologists with problematic space mining issues. The paper concludes that now is the time to discuss these issues, prior to the industrialisation and exploitation of unique celestial bodies. [ABSTRACT FROM AUTHOR]

In developing countries, one-quarter of young women have suffered from anemia. However, the available studies in Ethiopia have been usually used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of anemia among youth girls in Ethiopia. A total of 5642 weighted samples of young girls from the 2016 Ethiopian Demographic and Health Survey dataset were utilized. The data underwent preprocessing, with 80% of the observations used for training the model and 20% for testing. Eight machine learning algorithms were employed to build and compare models. The model performance was assessed using evaluation metrics in Python software. Various data balancing techniques were applied, and the Boruta algorithm was used to select the most relevant features. Besides, association rule mining was conducted using the Apriori algorithm in R software. The random forest classifier with an AUC value of 82% outperformed in predicting anemia among all the tested classifiers. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having more than 5 family size were the top attributes to predict anemia. Association rule mining was identified the top seven best rules that most frequently associated with anemia. The random forest classifier is the best for predicting anemia. Therefore, making it potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt anemia among youth girls. [ABSTRACT FROM AUTHOR]

Association rule mining utilizing metaheuristic algorithms is a prominent area of study in the field of data mining. However, when working with extensive data, conventional metaheuristic algorithms exhibit limited search efficiency and face challenges in deriving high-quality rules in multi-objective association rule mining. In order to tackle this issue, a novel approach called the adaptive Weibull distribution sparrow search algorithm is introduced. This algorithm leverages the adaptive Weibull distribution to improve the traditional sparrow search algorithm's capability to escape local optima and enhance convergence during different iterations. Secondly, an enhancement search strategy and a multidirectional learning strategy are introduced to expand the search range of the population. This paper empirically evaluates the proposed method under real datasets and compares it with other leading methods by using three association rule metrics, namely, support, confidence, and lift, as the fitness function. The experimental results show that the quality of the obtained association rules is significantly improved when dealing with datasets of different sizes. [ABSTRACT FROM AUTHOR]

In order to effectively prevent and control accidents, it is essential to trace back the causes of gas explosions in cities. The DT-AR(decision tree-association rule) algorithm is proposed as a quantitative analysis of gas accident features and causality. First, 210 gas explosion accident investigation reports were taken as samples. The gas accident causation system is divided into three aspects, including environmental factors, management factors and physical factors. Management factors were sorted into organizational-level and individual-level factors from the investigation reports. Second, the CART decision tree model was used to compare location features, organizational causality features, and individual causality features of the piped and bottled gas accidents, and a decision tree model with the gas system fault site as the root node was built to filter the key feature variables. In order to reveal factor correlations and deep-level causation, the Apriori algorithm is used to mine accident association rules. The combinations on the branches of the decision tree are used as constraints to filter the critical causality rule, which improves the efficiency of association rule screening and enhances prediction accuracy. The results demonstrate that the DT-AR algorithm can evaluate the importance of variables, quickly locate effective combinations of factors, and mine the complete causal chain. The association rule is screened based on the constraint of the key element combination of the decision tree, which compensates for the low efficiency of the Apriori algorithm for association rule mining. In addition, the accident-caused excavation results provide an effective path for gas companies, outsourced service companies and administrative departments to implement gas safety chain supervision, which can address the problem of gas accident safety management failures and provide decision support for accident prevention. [ABSTRACT FROM AUTHOR]

Understanding visitors' spatial choice behavior is important in developing effective policies to counteract overcrowdedness in attractive urban heritage areas. This research presents a comprehensive analysis of visitor location choice behavior, aiming to address two primary objectives. First, this paper investigates the relationship between visitor segments and the choice of particular Points of Interest (POIs). Second, this paper explores the impacts of visitors' experiences and visitor segments on their revisit intentions. We used a sample of 320 visitors who had been to Amsterdam within the last five years to collect data about their location choice behavior and intention to revisit after a recent visit to the city. Combining the revealed choices and intentions of pre-defined visitor segments obtained from a stated choice experiment, association rules are extracted to reveal differences in the patterns of behaviors related to the segment. The findings identify associations between various POIs, including museums such as the Rijksmuseum and Madame Tussauds, and visitor classes, which include "cultural attraction seekers", "selective sightseers", and "city-life lovers". Furthermore, binary logistic regression analysis reveals that affective experiences, such as feelings of comfort, happiness, and annoyance, have a significant influence on visitors' intentions to revisit the destination in the future. This research found that "cultural attraction seekers" and "selective sightseers" display a higher likelihood of considering a return visit to the city. [ABSTRACT FROM AUTHOR]

The increasing popularity of pigs has prompted farmers to increase pig production to meet the growing demand. However, while the number of pigs is increasing, that of farm workers has been declining, making it challenging to perform various farm tasks, the most important among them being managing the pigs' health and welfare. This study proposes a pattern mining-based pig behavior analysis system to provide visualized information and behavioral patterns, assisting farmers in effectively monitoring and assessing pigs' health and welfare. The system consists of four modules: (1) data acquisition module for collecting pigs video; (2) detection and tracking module for localizing and uniquely identifying pigs, using tracking information to crop pig images; (3) pig behavior recognition module for recognizing pig behaviors from sequences of cropped images; and (4) pig behavior analysis module for providing visualized information and behavioral patterns to effectively help farmers understand and manage pigs. In the second module, we utilize ByteTrack, which comprises YOLOx as the detector and the BYTE algorithm as the tracker, while MnasNet and LSTM serve as appearance features and temporal information extractors in the third module. The experimental results show that the system achieved a multi-object tracking accuracy of 0.971 for tracking and an F1 score of 0.931 for behavior recognition, while also highlighting the effectiveness of visualization and pattern mining in helping farmers comprehend and manage pigs' health and welfare. [ABSTRACT FROM AUTHOR]

The large amount of data generated every day makes necessary the re-implementation of new methods capable of handle with massive data efficiently. This is the case of Association Rules, an unsupervised data mining tool capable of extracting information in the form of IF-THEN patterns. Although several methods have been proposed for the extraction of frequent itemsets (previous phase before mining association rules) in very large databases, the high computational cost and lack of memory remains a major problem to be solved when processing large data. Therefore, the aim of this paper is three fold: (1) to review existent algorithms for frequent itemset and association rule mining, (2)to develop new efficient frequent itemset Big Data algorithms using distributive computation, as well as a new association rule mining algorithm in Spark, and (3) to compare the proposed algorithms with the existent proposals varying the number of transactions and the number of items. To this purpose, we have used the Spark platform which has been demonstrated to outperform existing distributive algorithmic implementations. [ABSTRACT FROM AUTHOR]

This study aims to explore the association between distracted driving (including cognitive, visual, operational, and auditory distractions) and multi-source features of ride-hailing drivers, and to discover frequent patterns of distracted driving. To achieve this, a natural driving experiment was conducted, and an association rule mining ('Apriori' algorithm) was used to uncover hidden rules between distracted driving and the multi-source features (including emotion, valence, arousal, driving tasks, cumulative driving hours, velocity, longitudinal acceleration, lateral acceleration, heading rate, presence of intersections, traffic status, and driving time of day). Results indicate that distracted driving is prevalent among ride-hailing drivers in specific scenarios, including trips without passengers, non-intersection sections, driving for over 4 h, and congested traffic conditions. The emotional state of drivers has also been found to have an interesting association with distracted driving. For instance, cognitive and operational distraction were highly associated with positive driving emotions, but auditory distraction caused by passenger interference was highly associated with negative driving emotions. Moreover, there are variations in distracted driving patterns across different categories. These findings can help ride-hailing platforms develop more scientific and effective distracted driving monitoring and prevention strategies. [ABSTRACT FROM AUTHOR]

The qualitative nature of co-deployed or "associated" metaphorical source domains in discourse has been extensively researched, often in terms of whether they share common conceptual roots. There are however few empirical studies on the strength of these associations and their implications. In mental healthcare activities like psychological interviews and counseling, for example, strongly associated sources may suggest unique "conceptual assemblages" that highlight underexplored (dis)similarities between clients or client groups, beyond the typical focus on isolated sources and frequencies. Our case study compares source domain associations produced by two interviewee groups – one meeting the diagnostic threshold for acute stress disorder (ASD), the other not – relating their first-hand experiences of traumatic events during the 2019 Hong Kong social unrest. The machine learning method of association rule mining is used to extract and quantify the top association rules in both groups. Results show i) a shared rule suggesting a highly schematic construal of trauma that nevertheless varies in instantiating details, and ii) several distinct rules that corroborates and lends further insight into the "agentive vs. non-agentive" characteristics exhibited by ASD vs. non-ASD individuals. Implications and future directions, including potential extensions to other discourse contexts, are discussed. [ABSTRACT FROM AUTHOR]

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In the iron and steel industry, evaluating the energy utilization efficiency (EUE) and determining the optimal energy matching mode play an important role in addressing increasing energy depletion and environmental problems. Electric Arc Furnace (EAF) steelmaking is a typical short crude steel production route, which is characterized by an energy-intensive fast smelting rhythm and diversified raw charge structure. In this paper, the energy model of the EAF steelmaking process is established to conduct an energy analysis and EUE evaluation. An association rule mining (ARM) strategy for guiding the EAF production process based on data cleaning, feature selection, and an association rule (AR) algorithm was proposed, and the effectiveness of this strategy was verified. The unsupervised algorithm Auto-Encoder (AE) was adopted to detect and eliminate abnormal data, complete data cleaning, and ensure data quality and accuracy. The AE model performs best when the number of nodes in the hidden layer is 18. The feature selection determines 10 factors such as the hot metal (HM) ratio and HM temperature as important data features to simplify the model structure. According to different ratios and temperatures of the HM, combined with k-means clustering and an AR algorithm, the optimal operation process for the EUE in the EAF steelmaking under different smelting modes is proposed. The results indicated that under the conditions of a low HM ratio and low HM temperature, the EUE is best when the power consumption in the second stage ranges between 4853 kWh and 7520 kWh, the oxygen consumption in the second stage ranges between 1816 m3 and 1961 m3, and the natural gas consumption ranges between 156 m3 and 196 m3. Conversely, under the conditions of a high HM ratio and high HM temperature, the EUE tends to decrease, and the EUE is best when the furnace wall oxygen consumption ranges between 4732 m3 and 5670 m3, and the oxygen consumption in the second stage ranges between 1561 m3 and 1871 m3. By comparison, under different smelting modes, the smelting scheme obtained by the ARM has an obvious effect on the improvement of the EUE. With a high EUE, the improvement of the A2B1 smelting mode is the most obvious, from 24.7% to 53%. This study is expected to provide technical ideas for energy conservation and emission reduction in the EAF steelmaking process in the future. [ABSTRACT FROM AUTHOR]

Despite increasing industrial advancements, fatal and severe accidents, such as "Falls", "Struck-by", "Hit by an object", "Be crushed", and "Caught-in/between" accidents, persist in developed countries, including Korea. Various methods, including risk assessment, monitoring systems, technology improvements, and safety education, are being implemented to reduce accidents. However, only a few studies have revealed the causes of accidents and their interrelationships; these studies are based on limited data. Korea recently published accident data using national statistical systems, including the construction safety management integrated information (CSI), enabling the analyses of major accident types. Here, we selected various representative accident cases to minimize the duplication of the data published from 2019 to 2023 and applied the Material, Method, Machine, or Man (4M) analysis method, a risk assessment technique, to perform an accident-type-based association rule mining (ARM) analysis of the accident factors. Through the ARM analysis, we quantitatively identified complex correlations for major accidents. Based on the 4M factors derived through this analysis, we improved a 2–4 model for accident causation and proposed safety management measures for each construction entity. [ABSTRACT FROM AUTHOR]

The precise classification of breast cancer subtypes is crucial for clinical diagnosis and treatment, yet early symptoms are often subtle. The use of multi-omics data from high-throughput sequencing can improve the classification accuracy. However, most research primarily focuses on the association between individual omics data and breast cancer, neglecting the interactions between different omics. This may fail to provide a comprehensive understanding of the biological processes of breast cancer. Here, we propose a novel framework called DiffRS-net for classifying breast cancer subtypes by identifying the association among different omics. DiffRS-net performs a differential analysis on each omics datum to identify differentially expressed genes (DE-genes) and adopts a robustness-aware Sparse Multi-View Canonical Correlation Analysis to detect multi-way association among DE-genes. These DE-genes with high levels of correlation are then used to train an attention learning network, thereby enhancing the prediction accuracy of breast cancer subtypes. The experimental results show that, by mining the associations between multi-omics data, DiffRS-net achieves a more accurate classification of breast cancer subtypes than the existing methods. [ABSTRACT FROM AUTHOR]

The rainfall characteristics of flash floods are highly sensitive to the joint impacts of different rainfall event features. In this study, we propose an objective framework for identifying rainfall characteristics for flash floods and assessing the risks while considering the combined impacts of multiple rainfall characteristics. The flash flood events are first classified into different types in terms of flood intensity and process using fuzzy C-means clustering with a subtractive clustering algorithm. Strong association rules between rainfall indices and flood types are subsequently identified based on an association rule mining method. The rainfall indices that strongly affect flash flood processes are obtained based on these association rules. Based on the results of the association analysis, the risks of different types of flash floods under different combinations of key rainfall indices are evaluated based on the Bayesian formula and copula function. The association rule analysis with single and multiple rainfall indices demonstrates that the maximum 12-h rainfall intensity and total antecedent cumulative rainfall are the major rainfall characteristics that affect flash flood processes in the study area. We also examine the risk of flash floods under different combinations of maximum 12-h rainfall intensity and total cumulative rainfall. This study provides an effective and quantitative approach to improve the risk analysis of flash floods and advances its application in the risk management of future flash floods. [ABSTRACT FROM AUTHOR]

Business is a war to get the attention you deserve from your enemies, and many competitors strive to gain a prominent position. Organizations are constantly seeking innovative ways to work to stay in a competitive business environment. Business process reengineering (BPR) is one of the most management approaches that are adopted by many organizations in order to achieve a dramatic increase in performance and cost reduction. Since the risks enfolded and failure rates related to BPR projects are very high, it is necessary to find ways to support success of BPR in a systematic approach. The major target of this article is to find the implementation of the proposed model to reengineer business processes (BPs) successfully via integrating critical success factors (CSFs) of BPR and BPs' performance. It is created to detect the inefficiencies and bottlenecks in the business process, decrease costs, time and increase quality of business processes, enhance financial environment and make an effective and efficient performance of the business process. It also applies a mining technique of association rule to examine the link between CSFs and several BPs and measures business processes' performance (intended BPR success) by process time, cycle time, quality and cost pre and post reengineering BPs. Thus, it uses a way to select the appropriate model for each business process. The proposed model will be implemented using a real world the Egyptian tax authority case study in order to prove its usefulness and efficiency. Then, inferred CSFs were applied according to each process, which proved the validity and success of the proposed model. [ABSTRACT FROM AUTHOR]

Educational system is significantly changing in today's world. Recently, the New Education Policy (NEP)-2020 isstarted implementing in India. Students can have various options for getting education according to their choices and requirements. NEP-2020 is more student-centric rather than making them compulsory to get the degree with prescribed syllabus. AI has a major role in NEP-2020. Data mining technology plays a vital role in this new higher education system. As the Higher Education Institutions are growing rapidly, it is necessary for them to impart quality education for enrollment of students. Institutions can maintain educational quality by improving their results. This can be achieved by predicting student academic performance with the help of data mining algorithms. Classification, clustering, regression and association rule mining are the data mining techniques which can be implemented on student dataset to predict the final grade. This study focuses on prediction of student performance using classification and regression data mining techniques. The aim of this literature review is to study various data mining tools, algorithms and the important attributes that affect the student academic performance. [ABSTRACT FROM AUTHOR]

An intrusion detection system (IDS) perform postcompromise detection of security breaches whenever preventive measures such as firewalls do not avert an attack. However, these systems raise a vast number of alerts that must be analyzed and triaged by security analysts. This process is largely manual, tedious, and time-consuming. Alert correlation is a technique that reduces the number of intrusion alerts by aggregating alerts that are similar in some way. However, the correlation is performed outside the IDS through third-party systems and tools, after the IDS has already generated a high volume of alerts. These third-party systems add to the complexity of security operations. In this paper, we build on the highly researched area of alert and event correlation by developing a novel hierarchical event correlation model that promises to reduce the number of alerts issued by an intrusion detection system. This is achieved by correlating the events before the IDS classifies them. The proposed model takes the best features from similarity and graph-based correlation techniques to deliver an ensemble capability not possible by either approach separately. Further, we propose a correlation process for events rather than alerts as is the case in the current art. We further develop our own correlation and clustering algorithm which is tailor-made to the correlation and clustering of network event data. The model is implemented as a proof of concept with experiments run on standard intrusion detection sets. The correlation achieves an 87% data reduction through aggregation, producing nearly 21,000 clusters in about 30 s.

Abstract Purpose Data mining has been used to help discover Frequent patterns in health data. it is widely used to diagnose and prevent various diseases and to obtain the causes and factors affecting diseases. Therefore, the aim of the present study is to discover frequent patterns in the data of the Kashan Trauma Registry based on a new method. Methods We utilized real data from the Kashan Trauma Registry. After pre-processing, frequent patterns and rules were extracted based on the classical Apriori algorithm and the new method. The new method based on the weight of variables and the harmonic mean was presented for the automatic calculation of minimum support with the Python. Results The results showed that the minimum support generation based on the weighting features is done dynamically and level by level, while in the classic Apriori algorithm considering that only one value is considered for the minimum support manually by the user. Also, the performance of the new method was better compared to the classical Apriori method based on the amount of memory consumption, execution time, the number of frequent patterns found and the generated rules. Conclusions This study found that manually determining the minimal support increases execution time and memory usage, which is not cost-effective, especially when the user does not know the dataset's content. In trauma registries and massive healthcare datasets, its ability to uncover common item groups and association rules provides valuable insights. Also, based on the patterns produced in the trauma data, the care of the elderly by their families, education to the general public about encountering patients who have an accident and how to transport them to the hospital, education to motorcyclists to observe safety points in Recommended when using a motorcycle.

The spatial structure of tourist flow represents the tourists' flow pattern, shows the difference of tourism resources, and provides the basis for the balance and sustainable development of tourism. Based on the digital footprint data obtained from the online travel notes on Qunar.com, this article describes and analyzes the spatial structure of tourism flow in Sanya City using the research methods of flow statistics, association rule mining, and social network analysis under the framework of anchor theory; further, it condenses the characteristics of different types of tourists at different time scales. The key findings include: (1) The spatial structure of Sanya's tourism flow is affected by the popularity of scenic spots, showing a flow pattern of "one core and two cores" and several core paths; the core area is located along the coastline, showing the characteristics of coastal tourism city. (2) In the spatial structure of Sanya's tourist flow, the distribution of core nodes is uneven, and the phenomenon of structural holes is obvious, showing the spatial pattern of uneven distribution of scenic spots and fierce competition among scenic spots. (3) In the structure network of Sanya's tourist flow, most tourists are medium-scale travelers, followed by small-scale travelers and large-scale travelers. (4) The tourist attraction system of Sanya's tourist flow is hierarchically distributed and has multiple anchor attractions. Different anchor attractions have different attractions to tourists of different types and time, except for beach tourist destinations and island areas. Therefore, targeted marketing for different types of tourists is a challenge for the future development of Sanya's tourism. In this article, the anchor point theory is introduced into the study of tourism flow. Based on the flow direction statistics, association rule mining and field investigation, the rules of the anchor point selection of tourism flow are established, verified, and supplemented by the social network analysis method. Theoretical support and quantitative data support complement each other. The application of anchor theory and association rule mining not only enriches the theoretical framework in the field of tourism flow, but it also scientifically determines the anchor points of the tourism flow's structure network. This is helpful to understand tourist behavior and mode, and it can provide targeted suggestions for the precision marketing of tropical coastal tourist cities.

This paper introduces the Web-Based Intelligent Packaging Evaluation (WIPE) platform, a novel approach to assess the performance of product and packaging systems within the e-commerce distribution sector. Unlike traditional methods that primarily rely on laboratory evaluations under controlled conditions, WIPE addresses the unique challenges posed by e-commerce distribution, such as increased handling points and unforeseen hazards that standard physical tests may not capture. Leveraging advanced machine learning algorithms and association rule mining, WIPE extracts insights about packaging defects directly from customer reviews on e-commerce platforms. Analyzing both images and text from these reviews establishes connections between frequently used words and the predicted damages, causes, and effects. This innovative approach was exemplified in two case studies involving laundry detergent liquid bottles and pods sold on the Amazon. The findings from these studies demonstrate WIPE’s capability to extract pertinent information from customer feedback and identify specific packaging defects and predict their potential causes. This integration of sentiment analysis and association rule mining into the packaging evaluation process marks a significant advancement in the field. The introduction of WIPE represents a transformative step in packaging evaluation, offering a more dynamic, real-world analysis that can significantly enhance product and packaging design, ultimately leading to improved customer satisfaction in the rapidly evolving e-commerce landscape.

An essential element of association rules is the strong confidence values that depend on the support value threshold, which determines the optimum number of datasets. The existing method for determining the support value threshold is carried out manually by trial and error; the user determines a support value such as 10%, 30%, or 60% according to their instincts. If the support value threshold is inappropriate, it produces useless frequent patterns, overburdens computer resources, and wastes time. The formula for predicting the maximum count of frequent patterns was 2n – 1, where $n$ is the number of distinct items in the dataset. This paper proposes a new SDFP-growth algorithm that does not require manual determination of the support threshold value. The SDFP-growth algorithm will perform dimensionality reduction on the original dataset that will generate level 1 and level 2 smaller datasets, thus automatically producing a dataset with an optimum amount of data with a minimum support value threshold. The proposed formula for predicting the maximum number of frequent patterns will become 2 $^{\vert A\vert }$ - 1, which is $\vert A \vert $ will always be smaller than $n$ . Experiments were performed on five various datasets, which reduced the number of data dimensions by more than 3% on the Level 1 dataset and more than 69% on the Level 2 dataset by maintaining the confidence value of the strong rules. In the execution time evaluated, we found an optimization of more than 2% on the level 1 dataset and more than 94% on the level 2 dataset.

The current state of Association Rule Mining (ARM) technology is heading towards a critical yet profitable direction. The ARM process uncovers numerous association rules, determining correlations between itemsets, forming building blocks that have led to revolutionary scientific discoveries. However, a high level of privacy is vital for protecting sensitive rules, raising privacy concerns. Researchers have recently highlighted challenges in the Privacy-Preserving Association Rule Mining (PPARM) field. Many studies have proposed workarounds for the PPARM dilemma by using metaheuristics. This paper conducts a systematic literature review on metaheuristic-based algorithms addressing PPARM challenges. It explores existing studies, providing insights into diverse metaheuristic approaches tackling PPARM problems. A detailed taxonomy is presented, offering a structured classification of metaheuristic-based algorithms specific to PPARM. This classification facilitates a nuanced understanding of the field by categorizing these algorithms into metaphor-based and non-metaphor-based groups, with a discussion of the nature of the representation schemes for each category identified in the survey. The review extends its analysis to encompass the latest applied approaches, highlighting the diversification of existing metaheuristic algorithms in the PPARM context. Moreover, common datasets and evaluation metrics identified from selected studies are documented to provide a deeper understanding of the methodological choices made by researchers in this domain. Finally, a discussion of existing challenges and potential future directions is presented. This review serves as a helpful guide that outlines previous research and presents potential future opportunities for metaheuristic-based algorithms in the context of PPARM.

Data analytics is an integral part of strategic decision making in various fields but not limited to business, education and healthcare systems. Existing research works focus on the discovery of itemsets with rare antecedents and consequent or frequent antecedents and consequent. Analysis of association among itemsets with rare antecedents and frequent consequent is equally important to gain valuable insights before making crucial decisions. Mining these itemsets from large datasets is time and resource intensive process. Expedition in the process of mining aids in quick decision making and hence, the entire dataset needs to be stored in the RAM. In this paper, a novel Weighted Binary Count Tree (WBIN-Tree) is proposed and implemented in CUDA to exploit the power of GPU and discover rules with rare antecedent and frequent consequent using parallel approach. WBIN-Tree stores the entire dataset in an abstract, complete and compact form in the RAM using single database scan. WBIN-Tree is compared with existing sequential and parallel algorithms by varying the data size and dimension. The performance evaluation of WBIN-Tree showed promising results, proving to be the most time and space efficient algorithm to store the entire large dataset in the RAM. However, based on the size of the GPU, the performance drops when executed on datasets with large dimensions which could be handled by processing the attributes in batches. Additionally, a case study is included to understand the importance of mining association rules with rare antecedent and frequent consequent by executing the algorithm on breast cancer dataset.

This paper proposes a variable-length Differential Evolution for Association Rule Mining. The proposed algorithm includes a novel representation of individuals, which can encode both numerical and discrete attributes in their original or absolute complement of the original intervals. The fitness function used is comprised of a weighted sum of Support and Confidence Association Rule Mining metrics. The proposed algorithm was tested on fourteen publicly available, and commonly used datasets from the UC Irvine Machine Learning Repository. It is also compared to the nature inspired algorithms taken from the NiaARM framework, providing superior results. The implementation of the proposed algorithm follows the principles of Green Artificial Intelligence, where a smaller computational load is required for obtaining promising results, and thus lowering the carbon footprint.

Aiming at the problem that substation robots cannot automatically find and analyze the fault equipment and carry out patrol inspection, a method of autonomous patrol inspection for ultra-high voltage (UHV) substation robots based on deep learning in cloud computing environment is proposed. Firstly, based on the cloud computing environment, an autonomous patrol system is designed to upload the data obtained by robots to the cloud platform for processing, so as to complete data analysis quickly and with high quality. Then, the deep learning algorithm (DL) is used for fault diagnosis, and the FP-growth algorithm is combined to realize the association mining of fault data, so as to clarify the patrol order of fault-related nodes. Finally, the improved ant colony algorithm (IAC) is used to optimize the path of the robot to complete the reliable inspection of the substation in the shortest time. Based on the selected UHV substation, the experimental analysis of the proposed method shows that the fault diagnosis error rate and time are about 4.3% and 14.2 s, respectively, and the patrol path is only 180.351 m, and the patrol time is 19.708 s, which can realize the optimal patrol of the robot. [ABSTRACT FROM AUTHOR]

Background: Classifying breast cancer subtypes is crucial for clinical diagnosis and treatment. However, the early symptoms of breast cancer may not be apparent. Rapid advances in high-throughput sequencing technology have led to generating large number of multi-omics biological data. Leveraging and integrating the available multi-omics data can effectively enhance the accuracy of identifying breast cancer subtypes. However, few efforts focus on identifying the associations of different omics data to predict the breast cancer subtypes. Results: In this paper, we propose a differential sparse canonical correlation analysis network (DSCCN) for classifying the breast cancer subtypes. DSCCN performs differential analysis on multi-omics expression data to identify differentially expressed (DE) genes and adopts sparse canonical correlation analysis (SCCA) to mine highly correlated features between multi-omics DE-genes. Meanwhile, DSCCN uses multi-task deep learning neural network separately to train the correlated DE-genes to predict breast cancer subtypes, which spontaneously tackle the data heterogeneity problem in integrating multi-omics data. Conclusions: The experimental results show that by mining the associations among multi-omics data, DSCCN is more capable of accurately classifying breast cancer subtypes than the existing methods. [ABSTRACT FROM AUTHOR]

The operations of the operators are important for nuclear safety, but conventional operating experience feedback and common data-driven methods make it difficult to explicitly find valuable information hidden in these operational sequences that can help the operator to provide advice at the operational level. During the nuclear power plant (NPP) operation, a large amount of historical operating data is accumulated, which records the operational sequences of the operators and the state parameters of equipment. Therefore, this paper proposes the use of association rule techniques to mine the NPP operating data to discover the operational characteristics of operators and reveal their possible impact on the NPP operation. This work helps to improve the operational performance of operators and prevent human-factor events. To this end, the concept of state switching values for describing the operating states of NPPs is proposed to enable the proposed method to be adapted to different practical application scenarios. A sequence segmentation method is proposed to be able to transform historical NPP operating data into a sequence data set for association rule mining. Furthermore, an ensemble algorithm based on sequence pattern mining and sequence rule mining and its postprocessing method are designed. The empirical study was carried out using 20 batches of historical operating data of the cold start-up. A total of 164 original association rules are generated using the proposed method and were analyzed by experts. The recommendations were made for 4 different cases that would improve the operational performance of the operators. [ABSTRACT FROM AUTHOR]

The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (UFAs) was analyzed using the Apriori algorithm of association rule mining. It was found that SDHW in Beijing were characterized by obvious spatial clustering, with hot spots predominately dispersed in urban areas and far suburbs, and cold spots mainly clustered in near suburbs. As for the associations with urban function areas, green space and park areas had significant effects on the positive sentiment in the study area, while a higher percentage of industrial areas had a greater impact on negative SDHW. When it comes to combined UFAs, our results revealed that the green space and park area combined with other functional areas was more closely related to positive SDHW, indicating the significance of promoting positive sentiment. Subdistricts with a lower percentage of residential and traffic areas may have a more negative sentiment. There were two main combined UFAs that have greater impacts on SDHW: the combination of residential and industrial areas, and the combination of residential and public areas. This study contributes to the understanding of improving community planning and governance when heat waves increase, building healthy cities, and enhancing urban emergency management. [ABSTRACT FROM AUTHOR]

Understanding air quality requires a comprehensive understanding of its various factors. Most of the association rule techniques focuses on high frequency terms, ignoring the potential importance of low- frequency terms and causing unnecessary storage space waste. Therefore, a dynamic genetic association rule mining algorithm is proposed in this paper, which combines the improved dynamic genetic algorithm with the association rule mining algorithm to realize the importance mining of low- frequency terms. Firstly, in the chromosome coding phase of genetic algorithm, an innovative multi-information coding strategy is proposed, which selectively stores similar values of different levels in one storage unit. It avoids storing all the values at once and facilitates efficient mining of valid rules later. Secondly, by weighting the evaluation indicators such as support, confidence and promotion in association rule mining, a new evaluation index is formed, avoiding the need to set a minimum threshold for high-interest rules. Finally, in order to improve the mining performance of the rules, the dynamic crossover rate and mutation rate are set to improve the search efficiency of the algorithm. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the unit point multi-information coding strategy in reducing the rule storage air, the effectiveness of mining the rules formed by the low frequency item set, and the effectiveness of combining the rule mining algorithm with the swarm intelligence optimization algorithm in terms of search time and convergence. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the above three aspects. The unit point multi-information coding strategy reduced the rule space storage consumption by 50%, the new evaluation index can mine more interesting rules whose interest level can be up to 90%, while mining the rules formed by the lower frequency terms, and in terms of search time, we reduced it about 20% compared with some meta-heuristic algorithms, while improving convergence. [ABSTRACT FROM AUTHOR]