Fuzzy Association Research Articles

Implementation of a Product-Recommender System in an IoT-Based Smart Shopping Using Fuzzy Logic and Apriori Algorithm

The Internet of Things (IoT) has recently become important in accelerating various functions, from manufacturing and business to healthcare and retail. A recommender system can handle the problem of information and data buildup in IoT-based smart commerce systems. These technologies are designed to determine users' preferences and filter out irrelevant information. Identifying items and services that customers might be interested in and then convincing them to buy is one of the essential parts of effective IoT-based smart shopping systems. Due to the relevance of product-recommender systems from both the consumer and shop perspectives, this article presents a new IoT-based smart product-recommender system based on an apriori algorithm and fuzzy logic. The suggested technique employs association rules to display the interdependencies and linkages among many data objects. The most common use of association rule discovery is “shopping cart analysis.” Customers' buying habits and behavior are studied based on the numerous goods they place in their shopping carts. As a result, the association rules are generated using a fuzzy system. The apriori algorithm then selects the product based on the provided fuzzy association rules. The results revealed that the suggested technique had achieved acceptable results in terms of mean absolute error, root-mean-square error, precision, recall, diversity, novelty, and catalog coverage when compared to cutting-edge methods. Finally, the method helps increase recommender systems' diversity in IoT-based smart shopping.

Research on automatic recognition of electronic communication information anomalies based on fuzzy association rules

Research on automatic recognition of electronic communication information anomalies based on fuzzy association rules

SMART-FCD: IOT DATA INTEROPERABILITY USING SENSOR BASED FUZZY LINKED RULES FOR CROSS DOMAIN APPLICATIONS

Internet of Things (IoT) is connected everywhere and enables massive information exchange between objects and people. The unified global IoT must manage a massive amount of data generated by these connected smart which arises the interoperability challenges such as lack of communication protocols, device support, and accepted open standards in smart environments. To combat these issues, a novel Smart-Fuzzy linked rules for Cross Domain application (Smart-FCD) framework is proposed to ensure interoperability by enabling efficient communication and data exchange between multiple platforms and systems. Initially, the heterogenous data from the sensors such as temperature sensors and humidity sensors, and the descriptions are implemented according to the Sensor Measurement List (SenML) language. After composing, semantic modelling will occur which converts the relational data into Resource Description Framework (RDF) format. The Linked Open Vocabulary for the Internet of Things (LOV4IoT) dataset is used to extract interoperable domain knowledge, which is then fed into the related sensor-based fuzzy rules. The IoT preset semantic models which utilise interoperable datasets and domain instances, which are updated via fuzzy association rules. Semantic Web of Things (SWoT) is a technology that assists in creating semantic-based IoT applications, thus IoT developers may use it to create intelligent applications. The sensor data, RDF simulation, evolution time and latency are some of the parameters that are used to evaluate the effectiveness of the proposed Smart-FCD methodology.

Quantitative Ensemble Interpretation of Membrane Paramagnetic Relaxation Enhancement (mPRE) for Studying Membrane-Associated Intrinsically Disordered Proteins.

An understanding of the functional role played by a membrane-associated intrinsically disordered protein (IDP) requires characterization of its heterogeneous conformations as well as its poses relative to the membranes, which is of great interest but technically challenging. Here, we explore the membrane paramagnetic relaxation enhancement (mPRE) for constructing ensembles of IDPs that dynamically associate with membrane mimetics incorporating spin-labeled lipids. To accurately interpret the mPRE Γ2 rates, both the dynamics of IDPs and spin probe molecules are taken into account, with the latter described by a weighted three-dimensional (3D) grid model built based on all-atom simulations. The IDP internal conformations, orientations, and immersion depths in lipid bilayers are comprehensively optimized in the Γ2-based ensemble modeling. Our approach is tested and validated on the example of POPG bicelle-bound disordered cytoplasmic domain of CD3ε (CD3εCD), a component of the T-cell receptor (TCR) complex. The mPRE-derived CD3εCD ensemble provides new insights into the IDP-membrane fuzzy association, in particular for the tyrosine-based signaling motif that plays a critical role in TCR signaling. The comparative analysis of the ensembles for wild-type CD3εCD and mutants that mimic the mono- and dual-phosphorylation effects suggests a delicate membrane regulatory mechanism for activation and inhibition of the TCR activity.

Retracted: Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm

Retracted: Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm

State feedback method of inter-turn short circuit in rotor winding of induction motor based on improved association rule algorithm

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.

Biomedical term extraction using fuzzy association

Biomedical term extraction using fuzzy association

Data mining algorithm based on Renyi fuzzy association rule: an application for the selection of suitable course

Data mining algorithm based on Renyi fuzzy association rule: an application for the selection of suitable course

Optimal pricing strategies and decision‐making systems in e‐commerce using integrated fuzzy multi‐criteria method

AbstractE‐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.

Exploring the characteristics of folk music tunes based on data mining model

Abstract In this paper, during the design process, some sample data are first extracted, and the distance between data is calculated to obtain the symmetric distance matrix along the diagonal. The model mutual information quantity is introduced, and the homogenization factor is detected after weight calculation. Next, analyze the rule weights based on the normalized mutual information quantity and use information entropy to identify abnormal data. Finally, find the minimum set with frequent item set and mine the fuzzy association rules. To verify the feasibility of the model, the results of exploring the characteristics of ethnic music tunes show that the neutral tone color is thin and the ethnic music melody line is soft in tune when the long and short melody lines are 9% and 12%, respectively. The number of strong and weak beats in the beat is 2-7 times, mixed with 2/4 and 3/8 beats alternately, and the musical tune is regular. The third-order interaction of wave amplitude variance is significant, and the two media of CD and phonograph help listeners understand and remember the music. Based on this, the digital model can improve the reproduction effect of folk music and can objectively target the transmission of folk music with national cultural characteristics.

Innovative Firm Performance Management Using a Recommendation System Based on Fuzzy Association Rules: The Case of Vietnam’s Apparel Small and Medium Enterprises

Purpose: This study aims to apply a classification algorithm based-on fuzzy association rules (FARs) to improve the effectiveness of firms' performance prediction problem. Particularly, this study investigates potential FARs exists between inputs and outputs of firms' performance management process. These extracted FARs could be used to help firm’s managers make better dicision to improve firm’s performance.
 
 Theoretical framework: Private enterprise development has been identified as key to Vietnam's economy that was commonly depended on state enterprise. For that, understanding and improving firms' performance and productivity is one of the most important tasks, from both macro and micro perspectives. There have been many studies on Vietnam's firm performance, but mostly relying on econometric methods that limit the understanding with structural equations. This study, instead, attempts to utilize new achievements of Artificial Intelligence (AI) for this task. Among AI techniques, fuzzy association rule is able to address the relationship between input factors and firm performance indicators. For each company, the finding FARs can be used to predict its performance and then change the business plan or react to improve weekness of organization.
 
 Design/Methodology/Approach: The proposal model is applied on data of small and medium-sized enterprises (SMEs) of the apparel industry in Vietnam in the period 2010-2015. The sample consist of a total of 23637 observation of Vietnam firms in apparel and textile industry and contains 16 main criterias for those firms.
 
 Finding: A recommendation system (RS) is constructed from disclosed FARs and is a key factor in a novel innovative firms' performance management process. The percentage of classified instances using the mining FARs is not quite high (about 82%), but it is not always the case. Vietnam’s apparel dataset includes rare classes of ROA, therefore applying only frequent FARs is not enough. This issue can be fixed by using both frequent and infrequent FARs. 
 
 Research, practical & social implications: The proposed model has a great opportunity to use not only in the small and medium-sized enterprises (SMEs) of the apparel industry but other industrial sectors. FARs support the well-understand of firm performance to firm’s manager and help them better to react. Besides, FARs could be used to create RSs that makes alerts about risk automatically.
 
 Originality/Value: The fact, our current study is the first to inspect the ability of FARs on SMEs of the apparel industry in Vietnam. This study provides theoritical potential knowledge and empirical evidence in the application of FARs technology in innovative firm’s management.

An integrated risk assessment model for the multi-perspective vulnerability of distribution networks under multi-source heterogeneous data distributions

The reliability of distribution network transmission corridors are vulnerable to different risk factors in both internal operation parameters and surrounding environmental conditions, and thus pinpoint evaluations of these impacting factors can further enhance their safety. To this end, this paper proposes an integrated assessment method, that is the multi-dimensional assessment with integrated fuzzy association (MDATFA). In this model, healthiness and importance indices of transmission lines are both defined to reflect the self-robustness and potential damages, respectively, and therefore to enable a two-dimensional assessment to incorporate the multi-source heterogeneous inputs. Firstly, healthiness indices for environmental characteristics are analyzed. On the one hand, discrete input features are processed by the established association rule mining model with rare variables (ARMrv) to mine and diagnose rarely occurred elements, and their healthiness contribution values can then be calculated. On the other hand, continuous features are handled by the designed hierarchical probabilistic fuzzy systems (HPFS). The weight assignments of these two expert models are adjusted by a gating network. Secondly, the importance indices for electrical characteristics are calculated. Under the condition that electrical signal constraints are satisfied, the amount of load loss and lost users can be solved by the built distribution network fault recovery model which is according to heuristic rule and second-order cone (HRSO). Finally, the validity and robustness of this operational condition assessment model is verified by an empirical analysis in an actual 39-bus power distribution system.

A quantum-like text representation based on syntax tree for fuzzy semantic analysis

To mine more semantic information between words, it is important to utilize the different semantic correlations between words. Focusing on the different degrees of modifying relations between words, this article provides a quantum-like text representation based on syntax tree for fuzzy semantic analysis. Firstly, a quantum-like text representation based on density matrix of individual words is generalized to represent the relationship of modification between words. Secondly, a fuzzy semantic membership function is constructed to discuss the different degrees of modifying relationships between words based on syntax tree. Thirdly, the tensor dot product is defined as the sentence semantic similarity by combining the operation rules of the tensor to effectively exploit the semantic information of all elements in the quantum-like sentence representation. Finally, extensive experiments on STS’12, STS’14, STS’15, STS’16 and SICK show that the provided model outperforms the baselines, especially for the data set containing multiple long-sentence pairs, which confirms there are fuzzy semantic associations between words.

Kernel-Induced Possibilistic Fuzzy Associate Background Subtraction for Video Scene

The background subtraction (BGS) technique is popularly used for many surveillance systems, segmenting the foreground by subtracting the modeled background from the image sequences. The effectiveness of any BGS technique depends on the robustness of the constructed background model. It is to be noted that many BGS schemes are affected by the inclusion of either noisy pixels in background construction or parameters of generative models. In this regard, we propound an idea of a kernel-induced possibilistic fuzzy associated BGS scheme for local change detection from a fixed camera captured sequence. The proposed scheme follows two stages: background training and foreground segmentation. In the background construction stage, each pixel is modeled using a possibilistic fuzzy cost function in kernel-induced space. The use of the induced kernel function will project the low-dimensional data into a higher dimensional space and the use of the possibilistic function will construct a robust background model based on the density of the data in the temporal domain avoiding the noisy and outlier points. The performance of the proposed scheme is tested on three benchmark databases. The effectiveness of the proposed scheme is evaluated on different performance evaluation measures: precision, recall, F-measure, and average similarity. We corroborate our findings by comparing them against 19 state-of-the-art existing BGS techniques.

WITHDRAWN: Short term optimal scheduling of a virtual power plants integrated with electrothermal hybrid energy storage system using an effective adaptive multi-objective differential evolution algorithm

virtual power plants (VPPs) in the power distribution grid play an important role in balancing the power and heat generation and consumption in residential, commercial, and industrial sectors. This study proposes VPPs for risk-averse energy exchange of resources and customers in the presence of the hybrid storage system. An appropriate energy management program is presented to make a balance between generators and consumers in VPPs considering generation uncertainties including electricity demand and price forecasting in the electricity market and renewable energy uncertainties. Hence, monetary exchange methods such as day-ahead agreements are proposed for energy exchange among VPPs. The expanded exchange structure of the suggested framework simplifies the renewable energy's sources to the electricity market despite its small-scale and non-dispatchable features. Also, a novel model is presented for short-term programing of virtual power plant considering technical and economic uncertainties. Given that VPPs can enter the electricity market based on time-of-use (TOU) pricing strategy and participation in demand response (DR) program, minimizing the expected cost of energy generation and environmental pollution is considered as the objective function. A multi-objective efficient model of energy management employing the machine speed scaling mechanism is suggested. An effective adaptive multi-objective differential evolution (AMODE) algorithm is presented to solve the multi-objective optimization problem. AMODE usages a novel Fitness Evaluation Mechanism (FEM) using dynamic reference point and fuzzy association randomness study to evaluate solutions in the evolutionary population by implementing the conditional value at risk (CVaR) method for risk management. The simulation studies and sensitivity analysis results are evaluated to confirm the effectiveness of the proposed model on a VPP in the island and grid-connected modes with and without participation in DR program. Finally, the conceptual results are discussed.

Data Mining and Analysis Algorithm of Smart City Network Information Resource Description Framework Based on Fuzzy Association Rules

Abstract Aiming at the problems of poor effect, low precision, and a long time frame in the current data mining analysis algorithm of the network information resource description framework (RDF), a data mining analysis algorithm of the network information RDF based on fuzzy association rules is proposed. Using association rule mining technology combined with fuzzy set theory, a fuzzy association rule algorithm is obtained to deal with quantitative data. Fuzzy c-means (FCM) is used to discretize continuous attributes, and the fuzzy concept pattern and its support and credibility are defined. Add a Mining Query flag to extend the SPARQL Protocol and RDF Query Language (SPARQL) syntax, define the user specified mining model, generate the corresponding project set and transaction, adopt the fuzzy association rule algorithm to generate fuzzy association rules, and realize the RDF data mining analysis of network information. The experimental results show that the network RDF data mining analysis effect of the proposed algorithm is good, which can effectively improve the network RDF data mining analysis accuracy and shorten the mining analysis time.

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

The literature on quality management system (QMS) assumes that product and process performance data are authentic and easily accessible. This assumption, while ideologically sound, is questionable in practise because the authenticity and accessibility of data cannot be guaranteed in many circumstances. Inaccurate, incomplete, inconsistent, and inaccessible data are common in supply chains and prevent the QMS from achieving its goal: assuring product and process quality to meet customer requirements. This study is one of the first to examine the impact of data quality and data latency on process control and quality analysis which are elemental parts of daily QMS activities, from a supply chain visibility (SCV) perspective. In this study, five propositions are made to show the relationships between technology, SCV, and data issues. More importantly, the study proposes a platform that integrates Blockchain (BC) technology, Industrial Internet of Things (IIoT), and Big Data to solve data problems in SCV and QMS. We further perform fuzzy association rule mining (FARM) to show how the platform can solve quality analysis problems and complete a closed-loop process control cycle in manufacturing. We also explain the contributions of the integrated platform to QMS from four theoretical perspectives. Finally, we discuss the limitations of the platform and provide recommendations for future research.

A Recommendation System in E-Commerce with Profit-Support Fuzzy Association Rule Mining (P-FARM)

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.

Learning positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracy

Nowadays, the call for transparency in Artificial Intelligence models is growing due to the need to understand how decisions derived from the methods are made when they ultimately affect human life and health. Fuzzy Rule-Based Classification Systems have been used successfully as they are models that are easily understood by models themselves. However, complex search spaces hinder the learning process, and in most cases, lead to problems of complexity (coverage and specificity). This problem directly affects the intention to use them to enable the user to analyze and understand the model. Because of this, we propose a fuzzy associative classification method to learn classifiers with an improved trade-off between accuracy and complexity. This method learns the most appropriate granularity of each variable to generate a set of simple fuzzy association rules with a reduced number of associations that consider positive and negative dependencies to be able to classify an instance depending on the presence or absence of certain items. The proposal also chooses the most interesting rules based on several interesting measures and finally performs a genetic rule selection and adjustment to reach the most suitable context of the selected rule set. The quality of our proposal has been analyzed using 23 real-world datasets, comparing them with other proposals by applying statistical analysis. Moreover, the study carried out on a real biomedical research problem of childhood obesity shows the improved trade-off between the accuracy and complexity of the models generated by our proposal.

Solar Powered Vibration Propagation Analysis System using nRF24l01 based WSN and FRBR

Prevention of the effects caused by natural disasters such as earthquakes and landslides requires analysis of vibration propagation. In outdoor applications, internet sources such as WIFI are not always available, so it requires alternative data communications such as nRF24l01. The system also requires a portable power source such as solar power. This research aims to develop a vibration propagation analysis system based on the nRF24l01 wireless sensor network and solar power by implementing the fuzzy rule-based regression (FRBR) algorithm. The system consists of two piezoelectric and nrf24l01 vibration sensors. The system also uses a third node equipped with temperature and soil moisture sensors, air temperature and humidity, and light intensity as environmental variables. The evaluation results show the Quality of Services (QoS) results with a throughput of 99.564%, PDR 99.675%, and a delay of 0.0073s. The Fuzzy Association Rule (FAR) extraction results yield nine rules with average support of 0.319 and confidence of 1 for vibration propagation. The availability of solar power was evaluated with an average current value of 0.250A and a voltage of 3.266V. The results of FRBR are based on the propagation of the vibration that propagated and produced a mean square error (MSE) of 0.141 and a mean absolute error (MAE) of 0.165. The correlation matrix and FAR results show that only soil moisture has a major effect on the magnitude and duration of propagation. However, other variables can regress soil moisture with MSE 0.232 and MAE 0.287.