Fuzzy Association Research Articles

Fuzzy Association of an Intrinsically Disordered Protein with Acidic Membranes.

Many physiological and pathophysiological processes, including Mycobacterium tuberculosis (Mtb) cell division, may involve fuzzy membrane association by proteins via intrinsically disordered regions. The fuzziness is extreme when the conformation and pose of the bound protein and the composition of the proximal lipids are all highly dynamic. Here, we tackled the challenge in characterizing the extreme fuzzy membrane association of the disordered, cytoplasmic N-terminal region (NT) of ChiZ, an Mtb divisome protein, by combining solution and solid-state NMR spectroscopy and molecular dynamics simulations. While membrane-associated NT does not gain any secondary structure, its interactions with lipids are not random, but formed largely by Arg residues predominantly in the second, conserved half of the NT sequence. As NT frolics on the membrane, lipids quickly redistribute, with acidic lipids, relative to zwitterionic lipids, preferentially taking up Arg-proximal positions. The asymmetric engagement of NT arises partly from competition between acidic lipids and acidic residues, all in the first half of NT, for Arg interactions. This asymmetry is accentuated by membrane insertion of the downstream transmembrane helix. This type of semispecific molecular recognition may be a general mechanism by which disordered proteins target membranes.

Early warning of enterprise finance risk of big data mining in internet of things based on fuzzy association rules

As the big data, Internet of Things, cloud computing, and other ideas and technologies are integrated into social life, the big data technology can improve the corporate financial data processing. At the same time, with the fiercer competition between enterprises, investors and enterprises have paid more attention to the role of financial crisis warning in corporate management. The work selected the multiple financial indicators based on big data mining in Internet of Things. The rules between all financial indicators were found to choose more representative financial risk indicators. Then the frequent fuzzy option set was determined by FCM (fuzzy cluster method), parallel rules, and parallel mining algorithm, thus obtaining the fuzzy association rules that satisfy the minimum fuzzy credibility. Finally, the relevant data of listed companies were selected to analyze the corporate financial risks, which verified the method proposed in the work.

Remote Identity Verification Using Gait Analysis and Face Recognition

Biometric identification has verified its effectiveness in personal identity verification because of the uniqueness and noninvasion. In this research, we tend to apply the detection of biometric information to a remote sensing system for the purpose of security area monitoring. Our system is established by collecting signals from the coming individuals via the remote measurement in the specific condition where both kinds of data are detected to determine the identity. Specifically, the measuring of gait signals and facial images is integrated to provide a way of improving the detection accuracy and the robustness. In addition, the fuzzy association rule (FAR) is employed for data analysis in line with the outcomes of different methods. As such, the signals are integrated and transmitted for further processing and remote identification. Experiments are conducted to demonstrate the capability of the proposed system. With the training data increases, a high detection accuracy of 95.2% is obtained, which makes it a promising basis for the realization of remote identity verification.

Reinforcement Learning-Based Data Association for Multiple Target Tracking in Clutter.

Data association is a crucial component of multiple target tracking, in which each measurement obtained by the sensor can be determined whether it belongs to the target. However, many methods reported in the literature may not be able to ensure the accuracy and low computational complexity during the association process, especially in the presence of dense clutters. In this paper, a novel data association method based on reinforcement learning (RL), i.e., the so-called RL-JPDA method, has been proposed for solving the aforementioned problem. In the presented method, the RL is leveraged to acquire available information of measurements. In addition, the motion characteristics of the targets are utilized to ensure the accuracy of the association results. Experiments are performed to compare the proposed method with the global nearest neighbor data association method, the joint probabilistic data association method, the fuzzy optimal membership data association method and the intuitionistic fuzzy joint probabilistic data association method. The results show that the proposed method yields a shorter execution time compared to other methods. Furthermore, it can obtain an effective and feasible estimation in the environment with dense clutters.

A Novel Fuzzy Frequent Itemsets Mining Approach for the Detection of Breast Cancer

Breast cancer, a type of malignant tumor, affects women more than men. About one-third of women with breast cancer die of this disease. Hence, it is imperative to find a tool for the proper identification and early treatment of breast cancer. Unlike the conventional data mining algorithms, fuzzy logic-based approaches help in the mining of association rules from quantitative transactions. In this study, a novel fuzzy methodology, IFFP (improved fuzzy frequent pattern mining), based on a fuzzy association rule mining for biological knowledge extraction, is introduced to analyze the dataset in order to find the core factors that cause breast cancer. It is determined that the factor, mitoses, has low range of values on both malignant and benign, and hence it does not contribute to the detection of breast cancer. On the other hand, the high range of bare nuclei shows more chances for the presence of breast cancer. Experimental evaluations on real datasets show that the proposed method outperforms recently proposed state-of-the-art algorithms in terms of runtime and memory usage.

Noise-tolerant Image Restoration with Similarity-learned Fuzzy Association Memory

Noise-tolerant Image Restoration with Similarity-learned Fuzzy Association Memory

The Principle of Fuzzy Negative Association Rule and Its Experiment Simulation

Energy conservation optimization control is an important way to reduce the energy emission and save energy in coal-fired power plants. There is a large amount of potential knowledge and information in the massive data in the real-time historical database of thermal power plant, which can be used for the control of energy conservation and optimization. This paper is mainly around the fuzzy association rules based on the traditional method in power plant, the application of energy saving optimization control for fuzzy association rules in specific research, and the simulation test data of the power plant operation.

Fuzzy Association Rule Classification System For Human DNA profile Identification

Recently, numerous identification methods are adopted in human identification systems particularly for forensic affairs. These methods are used to reduce the human efforts, related errors, cost, and consumed time in addition to increase the accuracy of human identification systems. The definition of STR DNA profile of a person’s, is associated to the reduction or detection of criminal investigation associated to the identification of the suspect, or to an investigation in counter terrorism as well as in the National Security Service. DNA is used for disaster victims or suspected individuals as a main means of identification. It possesses a high degree of verification in the courts when there is biological evidence, and is also used to identify persons accused and innocent people in criminal investigation with incredible accuracy. This thesis proposes a forensic human identification system based on Deoxyribonucleic Acid (DNA) classification. This system is depend on the concept of fuzzy logic and data mining in identification, that play role important in established a plenty of effective applications in DNA profile analysis and human identification, and reduce error or prevent and so they are chosen for their high accuracy. The main contribution of the presented system is, used association rules for classification the DNA motifs in addition to the fuzzy inference to correct errors, that can offer a high range of useful information. This information is permanently used to solve the crimes and identify damaged bodies. Additionally, this overcomes the lack problem in database information. It is important to note that the proposed system includes DNA based human identifications to cover all investigation cases. The aim of this thesis is to build a forensic human identification system that uses data mining techniques to access dataset by enforce particular algorithms for discovering advisable information or good patterns from present datasets for certain objective. To improve the data mining work, fuzzy logic is used to increase the flexibility for supporting DNA profile forensic human identification in making decisions. It utilizes sixteen Short Tandem Repeats (STRs) DNA loci used in identification methods for Population. It is important to note that the proposed system has been verified In terms of matching of provided samples with the samples stored in the database through conducting several experiments. The results were satisfactory and characterized by large percentage and high accuracy. Finally, performance of this system is evaluated and in turns the proposed system proves its capability in forensic human identification and scalability to handle huge amounts of data.

A Fuzzy Association Rules Mining Analysis of the Influencing Factors on the Failure of oBike in Taiwan

The sharing economy has become an important issue in recent years. Many researchers have paid attention to its application around the world. The sharing of bikes, as one of the major applications of the sharing economy, has shown its advantage in the realm of environmental protection and low energy consumption. However, bike-sharing system has encountered problems in certain regions. This arouses the concern about the sustainable development of the bike-sharing system. This research focused on the failure case of oBike in Taiwan. This research used text mining and fuzzy association rules mining methods to evaluate Taiwan’s public opinion about the oBike in order to verify the reasons for oBike’s failure in Taiwan. This study also made a comparison between the bike-sharing system in Mainland China and Taiwan. The research results explored the factors of oBike’s failure in Taiwan and showcased the problems of bike-sharing systems in different regions. The research results also offer useful information for bike-sharing companies and the authorities concerned in order to develop a sustainable bike-sharing system.

Cross-Border E-Commerce Personalized Recommendation Based on Fuzzy Association Specifications Combined with Complex Preference Model

Since cross-border e-commerce involves the export and import of commodities, it is affected by many policies and regulations, resulting in some special requirements for the recommendation system, which makes the traditional collaborative filtering recommendation algorithm less effective for the cross-border e-commerce recommendation system. To address this issue, a simple yet effective cross-border e-commerce personalized recommendation is proposed in this paper, which integrates fuzzy association rule and complex preference into a recommendation model. Under the constraint of fuzzy association rules, a hybrid recommendation model based on user complex preference features is constructed to mine user preference features, and personalized commodities recommendation is realized according to user behavior preference. Compared with the traditional recommendation algorithm, the improved algorithm reduces the impact of data sparsity. The experiment also verifies that the improved fuzzy association rule algorithm has a better recommendation effect than the existing state-of-the-art recommendation models. The recommendation system proposed in this paper has better generalization and has the performance to be applied to real-life scenarios.

Practical Moving Target Detection in Maritime Environments Using Fuzzy Multi-sensor Data Fusion

As autonomous ships become the future trend for maritime transportation, it is of importance to develop intelligent autonomous navigation systems to ensure the navigation safety of ships. Among the three core components (sensing, planning and control modules) of the system, an accurate detection of target ships’ navigation information is critical. Within a typical maritime environment, the existence of sensor noises as well as the influences generated by varying environment conditions largely limit the reliability of using a single sensor for environment awareness. It is therefore vital to use multiple sensors together with a multi-sensor data fusion technology to improve the detection performance. In this paper, a fuzzy logic-based multi-sensor data fusion algorithm for moving target ships detection has been proposed and designed using both AIS and radar information. A two-stage fuzzy logic association method has been particularly developed and integrated with Kalman filtering to achieve a computationally efficient performance. The effectiveness of the proposed algorithm has been tested and validated in simulations where multiple target ships are transiting with complex movements.

Research on Fault Monitoring Technology of Distribution Network Based on Fuzzy Association Rules Mining

In order to monitor the operation status of distribution network in real time and ensure the security and stability of distribution internet of things, a fault monitoring method based on fuzzy association rule mining was proposed. Fuzzy association rules mining was used to build the normal state parameter model of distribution network. The degree of interest index replaced the traditional index, aiming to improve the association degree between the rule parameters and form an effective expert knowledge base. According to the rules in the expert knowledge base, the normal state data of distribution network were obtained by fuzzy reasoning. Further, the state similarity function between the monitoring states and the normal states was used as the state judgement index. When the similarity is lower than the threshold, the fault alert should be triggered. Taking the single-phase ground fault data of distribution network simulated by MATLAB Simulink as an example, it was verified that the proposed method can effectively monitor the single-phase ground fault.

A strong intuitionistic fuzzy feature association map-based feature selection technique for high-dimensional data

In this work, a graph-based approach has been adopted for feature selection in case of high-dimensional data. Feature selection intends to identify an optimal feature subset to solve the given learning problem. In an optimal feature subset, only relevant features are selected as “members” and features that have redundancy are considered as “non-members”. This concept of “membership” and “non-membership” of a feature to an optimal feature subset has been represented by a strong intuitionistic fuzzy graph. The algorithm proposed in this work at first maps the feature set of the data as the vertex set of a strong intuitionistic fuzzy graph. Then the association between features represented as an edge-set is decided by the degree of hesitation between the features. Based on the feature association, the Strong Intuitionistic Fuzzy Feature Association Map (SIFFAM) is developed for the datasets. Then a sub-graph of SIFFAM is derived to identify features with maximal non-redundancy and relevance. Finally, the SIFFAM based feature selection algorithm is applied on very high dimensional datasets having features of the order of thousand. Empirically, the proposed approach SIFFAM based feature selection algorithm is found to be competitive with several benchmark feature selection algorithms in the context of high-dimensional data.

Exploration of Enhanced Fuzzy Association Rule Mining Algorithm

Exploration of Enhanced Fuzzy Association Rule Mining Algorithm

English

English

Application of data mining technology in alarm analysis of communication network

Nowadays, with the continuous development of science and technology, the social demand for the network is more and more, and with the continuous expansion of the network scale, the complexity of the network is increasing day by day. How to locate the fault accurately and quickly from a large number of alarm information has become a problem. To solve this problem, we must use computer technology to study the automatic analysis technology of alarm correlation. Therefore, this paper applies data mining technology to communication network alarm analysis, and based on fuzzy theory, it can accurately describe the relationship between network alarm and fault reason, and combines data mining technology with fuzzy theory to form the network alarm correlation analysis method of fuzzy association rule mining. On this basis, in order to mine the fuzzy association rules of the fuzzy alarm database directly and avoid the interference in the process of converting the alarm database to the transaction database, this paper proposes a dynamic time window fuzzy association rule mining algorithm. Through the simulation analysis, compared with the traditional data mining technology, the fuzzy association rules mining method combined with the fuzzy theory has better performance, and the further proposed dynamic time window fuzzy association rules mining algorithm greatly improves the accuracy of the association rules confidence, and has good application performance in the network alarm correlation analysis.

Intelligent privacy preservation electronic health record framework using soft computing

With the recent advancements in the technology, the biggest challenges are to protect the important sensitive data of electronic health records that are available over web. The real issues on EHRs to hide the sensitive huge data especially stored in distributed environment, and shared between numbers of stakeholders. It is an essential condition to eliminate the superfluous data and maintains the privacy of individual record stored in EHRs. An efficient mechanism is required to apply for privacy preservation on EHRs before it is disclosed to the public domain. The perturbation techniques are used to hide the data from unauthorized person for the security and privacy of a patient’s personal and sensitive information in electronic health records system. In this paper we propose a privacy preservation of electronic health records (PPEHR) framework to hide sensitive information using combination of fuzzy logic and perturbation association rules. In this framework, the privacy preservation is being tested using soft computing. Experimental results are demonstrated and verified with the help of machine learning algorithms.

Association rule mining using fuzzy logic and whale optimization algorithm

Association rule mining (ARM) is a well-known data mining scheme that is used to discover the commonly co-occurred itemsets from the transactional datasets. Two considerable steps of ARM are frequent item recognition and association rule generation. Minimum support and confidence measures are used in the generation of association rules. Many algorithms have been projected by the researchers to generate association rules. Fuzzy logic is incorporated to uncover the recurrent itemsets and interesting fuzzy association rules. In general, huge volume of datasets could be analyzed which in turn needs more number of database scans. In addition to this, all the transactions and items are not required for data analysis. Hence, the first step of this research work uses a dimensionality reduction technique which drastically reduces the size of the data set. This dimensionality reduction technique uses low variance and hash table methods. The proposed algorithm effectively identifies the significant transactions and items from the database. The issues of dimensionality reduction appear when the items in the databases are higher dimension than endure. The proposed algorithm reduces the irrelevant items and transactions from the transactional database. The proposed dimensionality reduction technique dimensionality reduction in transactions and items is compared with the extend frequent pattern (EFP) and intersection set theory EFP and dimensionality reduction using frequency count. Item reduction, transaction reduction, execution time and memory space are the performance factors. Second step proposes fuzzy and whale optimization for frequent item identification and association rule generation. The efficiency of the proposed algorithm is compared with particle swarm optimization genetic algorithm and fuzzy frequent itemset-Miner. Performance metrics used in this step are number of frequent items, association rules generated, execution time and memory required. Experimental results proved that the proposed techniques have produced the good results.

An interval fuzzy number-based fuzzy collaborative forecasting approach for DRAM yield forecasting

Most existing fuzzy collaborative forecasting (FCF) methods adopt type-1 fuzzy numbers to represent fuzzy forecasts. FCF methods based on interval-valued fuzzy numbers (IFNs) are not widely used. However, the inner and outer sections of an IFN-based fuzzy forecast provide meaning information that serves different managerial purposes, which is a desirable feature for a FCF method. This study proposed an IFN-based FCF approach. Unlike existing IFN-based fuzzy association rules or fuzzy inference systems, the IFN-based FCF approach ensures that all actual values fall within the corresponding fuzzy forecasts. In addition, the IFN-based FCF approach optimizes the forecasting precision and accuracy with the outer and inner sections of the aggregation result, respectively. Based on the experimental results, the proposed FCF-II approach surpassed existing methods in forecasting the yield of a dynamic random access memory product.

Semantic Classification of Images in Hierarchical Manner using Fuzzy Rules and HSVM Classifier

Hierarchical structure classification is more effective in concept based classification. The feature vector is varying with the different concepts, though it belongs to the same class. Images which are similar in appearance are not correlated semantically. Hence an author made an attempt to classify the images in the dataset in concept wise in a hierarchical manner. For this, an author manually clustered the same concept images in a layer by layer to explore the semantic correlation of the concepts. A number of nodes in the hierarchy tree represent the number of classes in the dataset. Each node is classified into sub-nodes by the fuzzy classifier using fuzzy association rules. Fuzzy inference system trained by the fuzzy rules base, which is generated by the fuzzy feature vector. The performance of the fuzzy classifier is validated with multiclass support vector machine classifier and hierarchical support vector machine classifier. The dataset used here is COREL dataset; an author selected 8 concepts for the classification. Keywords: Fuzzy classifier, fuzzy rule, hierarchical SVM, multiclass SVM, semantic concepts Cite this Article: Thirumala Lakshmi K, Usha Kingsly Devi K. Semantic Classification of Images in Hierarchical Manner using Fuzzy Rules and HSVM Classifier. Journal of Image Processing & Pattern Recognition Progress. 2020; 7(2): 33–54p.