Idiopathic pulmonary fibrosis (IPF) is an intractable lung disease that belongs to idiopathic interstitial pneumonia (IIP) with limited therapeutic options. Conventional patient stratification approaches often fail to integrate diverse data modalities, particularly heterogeneous electronic medical records (EMR) containing mixed discrete and continuous values, with omics data, or fail to extract the interpretable many-to-many relationships crucial for precision medicine. We introduce subset binding (SB), a novel unsupervised algorithm that extends fuzzy association rule mining to robustly integrate heterogeneous clinical data (EMR) and omics data. This framework is uniquely designed to identify clinically meaningful patient subgroup patterns and discover associated molecular signatures based on observable symptoms rather than relying on ambiguous conventional diagnostic categories, such as IIPs. Applying SB to a dataset including 602 samples (from 403 IIPs including IPF patients and 39 healthy controls), we successfully identified 20 proteins linked with key IPF clinical features. Network-based pathway analysis nominated tyrosine kinases as critical drug target candidates, leading to the proposal of ponatinib, a multi-kinase inhibitor, as a candidate therapeutic. Functional validation using a TGF-β-induced epithelial-mesenchymal transition (EMT) model confirmed ponatinib's ability to at least partially suppress TGF-β-induced EMT. This inhibitory effect is consistent with the anti-fibrotic mechanism of the existing IPF drug, nintedanib, and reinforces prior evidence supporting ponatinib's anti-fibrotic property. This study demonstrates that SB enables transparent, reproducible, and robust, molecularly defined patient stratification from multimodal patient data. By establishing a data-driven framework that focuses on observation-based rules, this work lays the critical foundation for future prognostic validation and tailored treatment strategies, offering clinically actionable insights and therapeutic discovery in diagnostically ambiguous diseases like IPF, with ponatinib emerging as a compelling repurposing candidate. Significance statement Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with limited therapeutic options. IPF is classified as idiopathic interstitial pneumonia (IIP), but distinguishing it from other similar diseases in IIP is not straightforward. The ambiguities in distinguishing IPF from other IIPs necessitate the identification of molecules associated with specific clinical features, rather than relying on solely on diagnosis. Existing methods for multi-omics data analysis often fail to effectively integrate heterogeneous data - such as EMR (containing mixed discrete and continuous values) and omics - or to extract many-to-many molecular-phenotypic relationships. We developed subset binding (SB), a novel, interpretable unsupervised machine learning method to specifically address these technical limitations by integrating EMR and omics data. Our approach successfully detected proteins in serum extracellular vesicles associated with IPF-related features, highlighted several tyrosine kinases as potential drug targets, and proposed the multi-kinase inhibitor ponatinib as a compelling candidate for drug repurposing. This data-driven framework establishes a scalable and interpretable foundation for biomarker and drug target discovery for intractable diseases whose mechanisms are not fully understood.

Despite motors being essential general-purpose machinery, current methods in motor equipment health management fail to provide timely and effective responses to abnormal conditions. Case-based reasoning (CBR) can address sudden failures swiftly through structured knowledge bases, yet it faces challenges such as difficulties in feature selection and inaccurate similarity measurements, especially with class-imbalanced data. This study proposes a novel feature attribute reduction module using an improved fuzzy association rule mining (FARM) in the CBR framework. By introducing fuzzy theory to handle sharp boundaries in datasets and refining the traditional probability-based association rule mining, the F-Apriori algorithm is established to effectively identify the correlation between feature attributes and failure types in class-imbalanced datasets, enabling the extraction of strongly associated features and the optimal attribute sets. Finally, comparative experiments demonstrate that the proposed model achieves lower computational time costs and higher accuracy. Specifically, the computational time of the proposed model is reduced by 231.4 ms and 168 ms, respectively, compared to the baseline methods. The model attains an average accuracy of 90.2%, outperforming the counterparts by 4% and 2.6%, respectively. This approach effectively enhances current health management technologies and significantly improves the operational management of mechanical equipment in industrial settings.

Background/Objective: Frontline healthcare staff who contend diseases and mitigate their transmission were repeatedly exposed to high-risk conditions during the COVID-19 pandemic. They were at risk of mental health issues, in particular, psychological stress, depression, anxiety, financial stress, and/or burnout. This study aimed to investigate and evaluate the occupational stress of medical doctors, nurses, pharmacists, physiotherapists, and other hospital support crew during the COVID-19 pandemic in Saudi Arabia. Methods: We collected both qualitative and quantitative data from a survey given to public and private hospitals using methods like correspondence analysis, cluster analysis, and structural equation models to investigate the work-related stress (WRS) and anxiety of the staff. Since health-related factors are unclear and uncertain, a fuzzy association rule mining (FARM) method was created to address these problems and find out the levels of work-related stress (WRS) and anxiety. The statistical results and K-means clustering method were used to find the best number of fuzzy rules and the level of fuzziness in clusters to create the FARM approach and to predict the work-related stress and anxiety of healthcare staff. This innovative approach allows for a more nuanced appraisal of the factors contributing to work-related stress and anxiety, ultimately enabling healthcare organizations to implement targeted interventions. By leveraging these insights, management can foster a healthier work environment that supports staff well-being and enhances overall productivity. This study also aimed to identify the relevant health factors that are the root causes of work-related stress and anxiety to facilitate better preparation and motivation of the staff for reorganizing resources and equipment. Results: The results and findings show that when the financial burden (FIN) of healthcare staff increased, WRS and anxiety increased. Similarly, a rise in psychological stress caused an increase in WRS and anxiety. The psychological impact (PCG) ratio and financial impact (FIN) were the most influential factors for the staff's anxiety. The FARM results and findings revealed that improving the financial situation of healthcare staff alone was not sufficient during the COVID-19 pandemic. Conclusions: This study found that while the impact of PCG was significant, its combined effect with FIN was more influential on staff's work-related stress and anxiety. This difference was due to the mutual effects of PCG and FIN on the staff's motivation. The findings will help healthcare managers make decisions to reduce or eliminate the WRS and anxiety experienced by healthcare staff in the future.

In the current landscape of data analytics, Fuzzy Association Rule Mining (FARM) is being extensively employed to produce interpretable fuzzy rules. In the literature, various toolkits exist that assist practitioners in performing FARM-based inference on their dataset, but a comprehensive open-source GUI-enabled toolkit that facilitates Generative AI-based inference, customizable fuzzy partitioning and brute-force FARM Rules Explorer is scarce in existing toolkits. This research aims to bridge this gap by offering a comprehensive technical suite for FARM and Weighted FARM (WFARM) using the Fuzzy Apriori algorithm to aid researchers and practitioners.

Fuzzy Association Rule Mining (FARM) extends traditional ARM by evaluating and pruning rules based on interestingness measures to identify relevant patterns for various applications. The focus of this paper is to explore the application of FARM techniques demonstrating its algorithmic implementation in a meteorological dataset. Three major algorithms known as fuzzy Apriori, FTDA (Fuzzy Transaction Data-Mining Algorithm) and CFARM Composite Fuzzy Association Rule Mining) are experimented and analyzed. The experiment uses a real meteorological dataset spanning twenty years consisting some important attributes of weather such as rainfall, temperature, relative humidity, wind speed and bright sunshine hours of the North Bank Plain Zone (NBPZ) of the Brahmaputra River in Assam, India. The collected dataset is pre-processed into a transaction dataset and converted into a fuzzy dataset using membership functions. The three FARM algorithms are subsequently employed to uncover associations among various attributes within the fuzzy meteorological dataset. This study analyzes experimental results from three algorithms, focusing on factors like rule generation, computation time, and memory consumption. While Fuzzy Apriori provides comprehensive rule generation, it comes at the cost of higher computation time and memory usage. FTDA and CFARM, on the other hand, offer more efficient and significant rule generation, making them more suitable for large-scale, complex data analysis. The findings of this paper can contribute to the development of resilient and efficient data mining frameworks, enhancing the decision-making process for stakeholders in the meteorological domain. Thus, the paper introduces a new method for analyzing meteorological data using Fuzzy Association Rule Mining (FARM) techniques.

Live streaming of e-commerce platforms attracts consumers for their products/purchases and hence the familiarity is retained high amid different competitors. Fuzzy decision systems are incorporated to filter the streaming content of the platforms to improve consumer augmentations at different promotions. Therefore to support such augmentation and consumer building process, this article proposes a filtered sale streaming model to improve the circulation of new launches and to project the existing products through sustainable promotions. In this process, the comprehensive transition rule for product promotions and sale improvements is defined using fuzzy mining. The fuzzy process introduces the different performance members based on consumer access rate and sale count. The rule modifications are defined using the above factors’ decrease over filtered promotions to boost the augmentation. Using the highest possible member weights over a product, sale, and consumers, the linear improvements between the three factors are estimated over the closure observed at each sale interval. Thus, the streaming modifications and the product exposures are modeled using different mining rules adaptable for e-commerce platforms.

College students are expected to possess a higher level of education, and increasing numbers of job positions require higher proficiency in English. Students come from various wisdom, and their demands for learning, ways of thinking, and learning methods will vary. In higher education, Personalized English Language Teaching (PELT) is crucial for addressing the various requirements of students, boosting motivation with involvement, maximizing resource use, preparing them for future professions, and improving their learning outcomes. To provide students with personalized and effective training in higher education, Fuzzy associated Frequent Pattern-Growth (F2PG-PELT) has been proposed to discover personalized relationships and patterns in student data using fuzzy association rule mining. Secondly, the Frequent Pattern-Growth algorithm is used to identify frequent patterns in learner data that are pertinent and meaningful. The relationships and connections between language factors or student qualities are crucial to personalized education and can be identified using a candidate frequent pattern tree. Due to the diversity of teaching strategies, the fuzzy association rules are generated using minimum support and confidence measure. The proposed model develops high learning outcomes with self-assurance, improves language and communication skills, offers individualized learning opportunities, and fosters lifelong learning abilities. The experimental outcomes demonstrate that the suggested fuzzy association rule mining employed for the PELT model increases the student engagement ratio, teaching efficiency ratio, learning outcome ratio, and teacher involvement ratio compared to other state-of-the-art approaches.

Due to rapid information technology growth, teaching Chinese in higher education has changed, and Chinese literary majors have vigorously evolved. The key teaching difficulties are scalability, individualized teaching, and a lack of resources and methodologies. Research shows individualized education improves topic comprehension, cultural engagement, and learner interest. Fuzzy association rule mining uses fuzzy linguistic values and membership functions to provide more realistic results. Hence, an algorithm, EF-PCL2T, has been proposed to improve personalized Chinese language and literature teaching (PCL2T) using enhanced fuzzy (EF) Apriori association rule mining integrated with the genetic algorithm. Fuzzy Apriori association rule mining identified frequent itemsets with relevant learning patterns and produced applicable association rules from datasets with fuzzy or unclear information, capturing fluctuating itemset importance and providing a flexible representation of relationships to determine student preferences. From fuzzy-related data, a genetic algorithm optimizes skill sets and creates individualized lesson plans considering each student’s competency and preferences for adjusting to personalized teaching tactics. Testing shows that fuzzy enhancement association rule mining for the PCL2T model improves student retention, PET (personalized teaching efficiency), minimal support and confidence update with fuzzy rules, and student involvement compared to other state-of-the-art methods. Students agree that tailored Chinese language and literary instruction is possible. The improvement results show fuzzy rules with minimum confidence levels of 50% to 100%, highly correlated in this model, student retention ratio of 96%, improved assessment grade of various language skills by 40 marks, PTE analysis of 93%, and student involvement ratio of 97%.

Universities and colleges aim to provide students with a solid academic foundation. Quality instruction is one strategy for achieving the highest possible standard in the higher education system. Personalized teaching caters to each student by adapting the learning pace and method to their specific requirements. However, the present state of customized education in higher education resources prevents proper resources from being extracted due to a lack of multi-dimensional association analysis between students, circumstances, and materials. A hybrid personalized teaching system utilizing fuzzy association rules mining is the goal of this research to improve learning in higher education. Effective multi-dimensional association analysis among students, settings, and instructional materials is facilitated by the fuzzy association rules mining-based hybrid personalized teaching system (FARM-HPT). The proposed study conforms to AI standards, is based on fuzzy logic theories, and guarantees precise university-level resource discovery. The study builds on earlier work in data mining by presenting a new, learner-specific recommendation model for personalized teaching that uses FARM to ensure accurate resource recognition and efficient mining of instructional assets at the higher education level. This new approach generates fewer set comparisons and does them faster than the current standard. Focusing on experimental validation, the study shows that the FARM-HPT system can generate individualized lessons while overcoming the constraints of traditional information mining methods. These findings align with AI standards, which shows how important it is to validate new AI approaches using robust empirical evidence. The system ensures effective accuracy on various datasets: LFW (89.76%), JAOLAD (94.43%), OECD (95.43%) and OULAD (97.45%).

Fuzzy association rules with its linguistic annotations and human interpretable form, has provided a convenient extension of association concepts to quantified attributes. The applicability is extended by combining extraction of both positive and negative association rules. Interestingness measures are used to filter out the useful and correct set of actionable association rules from the larger set of rules mined by association rule mining algorithms. Many measures such as Support, Confidence, Conviction and Certainty Factor, with their own area of applicability and statistical significance are popular. The wide range of measures is usually based on frequency counts or probability of occurrence of certain attribute patterns. Binary attributes uses a 2×2 contingency table as the basis for defining different measures. This paper presents concept of fuzzy support matrix using fuzzy partitions, as a natural extension of contingency table for the different interestingness measures. Those can be defined in a uniform and consistent manner. It uses the existing interestingness measures defined in new form using fuzzy support and illustrate these concepts using known data sets. This paper represent active research directions aimed at advancing the capabilities, applicability, and efficiency of fuzzy association rule mining in handling modern data challenges across various domains. Keywords: Interestingness measures; Association Rules mining; Fuzzy sets.

Knowledge discovery in the form of rules has become a useful and meaningful practice in Data Mining. In the last three decades, Association Rule Mining has been considered one of the primary research activities. Introducing fuzzy mathematical theories provides another dimension to make association rules more user-friendly and expressive. Membership degrees and Linguistic Terms play a significant role in the fuzzification process. The use of Fuzzy set concepts in rule generation, however, needs more attention from the researchers in different application domains. This paper is based on the study and findings of some important Fuzzy Association Rule Mining (FARM) algorithms and their applications. Beginning with the overview of Fuzzy Set theory and FARM algorithms, this study gives an analysis and account of the intricacies of algorithm development and applications-based activities in the last three decades. Out of many algorithms, some important FARM algorithms are undertaken for the study and explained with examples. The examples are prepared from both real and synthetic data. Based on the study, some key features of leading algorithms are observed and highlighted. Different research issues and challenges related to FARM are found out and discussed for further research.

Knowledge discovery in weather forecasting: mining fuzzy image association rules with fine-tuned CNN and fuzzy HIFP algorithm

A framework for image-based counterfeit coin detection using pruned fuzzy associative classifier

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.

ABSTRACT This paper presents a comparative effectiveness analysis of fuzzy and non-fuzzy association rule mining (ARM). The corresponding motivation is the lack of relevant papers devoted to the effectiveness comparison between fuzzy and non-fuzzy ARM. The current research applies the results of fuzzy/non-fuzzy ARM to associative classification and uses the classification accuracy of the corresponding classifiers as the effectiveness measure. The research demonstrates that basic effectiveness comparison between fuzzy and non-fuzzy ARM does not necessarily speak in favour of fuzzy ARM. However, then the research demonstrates that fuzzy ARM has a distinctive ability to handle data inconsistencies, which results in the ability of corresponding fuzzy classifiers to not only provide class predictions but also indicate their certainty in the provided output. The research reveals some distinct correlation between classification accuracy and the degree of certainty in fuzzy associative classification: the greater certainty nearly always results in the better classification accuracy. This ability of fuzzy associative classifiers to indicate their certainty in the performed classification and the corresponding ability of fuzzy ARM to handle data inconsistencies are not applicable in non-fuzzy associative classifiers and ARM. Therefore, these abilities are used in the paper to substantiate the relevance of applying fuzzy logic in ARM.

Under the intensely competitive global economy, the new product development (NPD) has gradually changed from production-oriented to market-oriented. Therefore, it is an important factor to transform specific customers’ needs into shape appeal in products’ sale. The main purpose of this study is to analyze products’ Kansei knowledge by combining rough set theory (RST) and fuzzy association rule mining (FARM), thus providing decision support for NPD. The core Kansei needs that have a great impact on customers’ satisfaction is extracted in RST. FARM can identify the potential relationship between key Kansei attributes and product design attributes. The upright exercise bike is taken as the target case. It is found that the core customer needs of the exercise bike are “vitality”, “speed”, “luxury” and “stable”; while the “modern” which has no impact on emotional properties is deleted. Finally, the mapping relationship between the “vitality” Kansei image and the shape element is given priority. The result shows that the systematic design method combining RST and FARM has good predictive ability and significantly improves the efficiency of research and customers’ satisfaction.

Aiming at timely feedback and processing of asynchronous motor rotor windings, a feedback method based on an improved association rule algorithm of asynchronous motor rotor windings is studied. Based on the inter-turn short circuit mechanism of asynchronous motor windings, the fuzzy association rule mining method is used to mine the inter-turn short circuit mechanism of asynchronous motor windings. The method mines the rotor-side electrical parameters as the input of the deep neural network and uses the deep neural network to output the short-circuit state feedback results of the rotor windings of the asynchronous motor. The experimental results show that when the A-phase of the asynchronous motor is the faulty phase, the A-phase current of the rotor winding inter-turn short circuit increases significantly, and the phase angle difference deviation has a positive correlation, which effectively feedbacks the asynchronous motor rotor winding inter-turn short-circuit state.

Abstract E‐commerce online stores are now virtual platforms for connecting with millions of potential clients worldwide in the age of digitization. The marketing teams develop digital marketing tactics to attract traffic to their e‐commerce sites and increase sales volume. With the vast amount of data provided by the cloud, decisions that were previously made with a significant level of intuition based on the knowledge and experience of decision‐makers can now be backed using artificial intelligence algorithms. To identify the variables influencing pricing decisions for products launched on e‐commerce shopping sites and to develop variable pricing techniques for each product found on an e‐commerce site. This paper introduces a novel approach that applies Fuzzy association rule mining (FARM) and Fuzzy TOPSIS MCDM methodology. A B2B e‐commerce marketing store based in Hong Kong has created and implemented Smart‐Quo, a pricing decision support system for B2B e‐commerce retail businesses. After a six‐month trial period, there has been a substantial advance in the effectiveness and efficiency of choosing prices for each product. The case study illustrates the viability and potential advantages of implementing artificial intelligence tools in marketing management in the digital era.

Blockchain-IIoT-big data aided process control and quality analytics

E-commerce is snowballing with advancements in technology, and as a result, understanding complex transactional data has become increasingly important. To keep customers engaged, e-commerce systems need to have practical product recommendations. Some studies have focused on finding the most frequent items to recommend to customers. However, this approach fails to consider profitability, a crucial aspect for companies. From the researcher’s perspective, this study introduces a novel method called Profit-supported Association Rule Mining with Fuzzy Theory (P-FARM), which goes beyond just recommending frequent items and considers a company’s profit while making product suggestions. P-FARM is an advanced data mining technique that creates association rules by finding the most profitable items in frequent item sets. From the practitioners’ standpoints, this method helps companies make better decisions by providing them with more profitable products with fewer rules. The results of this study show that P-FARM can be a powerful tool for improving e-commerce sales and maximizing profit for businesses.