Fuzzy C-means Method Research Articles

Component-wise design method of fuzzy C-means clustering validity function based on CRITIC combination weighting

Component-wise design method of fuzzy C-means clustering validity function based on CRITIC combination weighting

Application of the Fuzzy C-Means Method in Grouping Heart Abnormalities Based on Electrocardiogram Medical Records

Heart disease is the main cause of death which can be diagnosed using an electrocardiogram. This study aims to classify heart defects using the Fuzzy C Means technique. The advantage of using Fuzzy C Means is that it is unsupervised and can reach a convergent cluster center under certain conditions. It is a clustering model that has the value of the objective function, number of iterations and completed time. In an unsupervised learning, the focus is more on exploring data such as looking for patterns in the data. Clustering itself aims to identify patterns of similar data to be grouped. It can be a solution to overcome the process of determining the risk of heart disease. The results showed that there were 10 data grouped into cluster 1 and 10 data into cluster 2. The first group (Cluster 1) consisted of patients with serial numbers 3,5,8,9,11,12,16,17,19,20, while the second group (Cluster 2) consisted of patients with serial numbers 1,2,4,6,7,10,13,14,15 and 18. Accuracy testing results in a success rate of 60%.

Short Circuit Failure Detection in Induction Motor Using Wavelet Transform and Fuzzy C-Means

Induction motors need to be monitored regularly because it involves the company's productivity. The induction motor monitoring method in this study uses a motor current variable which is transformed using the Discrete Wavelet Transform. Discrete Wavelet Transform (DWT) is used in this study because the results are satisfactory for detecting a short circuit in the stator winding of an induction motor. Of the many types and levels of discrete wavelet transforms, the haar wavelet transform at the third level is used in this study. Furthermore, the results of the discrete wavelet transform are processed using the Fuzzy C-means method. Fuzzy C-Mean (FCM) is the grouping approach that each part has a member degree of cluster according to the fuzzy logic algorithm. Motor modeling is shown in this article as normal condition, final fault current, and initial fault current. For this analysis, a combination of wavelet transform and Fuzzy C-means is used to classify motor currents into three motor states. The motor current is processed by Haar DWT level 3 to generate a high frequency signal. Then the high frequency signal is processed to get the energy signal. The energy signal is then fed to Fuzzy C-means to identify its condition. The results show that fuzzy C-means produces an error of 0% for the normal case, 33.3% for the initial error case and 0% for the final error case.

Fuzzy based optimized itemset mining in high dimensional transactional database using adaptable FCM

One of the extremely deliberated data mining processes is HUIM (High Utility Itemset Mining). Its applications include text mining, e-learning bioinformatics, product recommendation, online click stream analysis, and market basket analysis. Likewise lot of potential applications availed in the HUIM. However, HUIM techniques could find erroneous patterns because they don’t look at the correlation of the retrieved patterns. Numerous approaches for mining related HUIs have been presented as an outcome. The computational expense of these methods continues to be problematic, both in terms of time and memory utilization. A technique for extracting weighted temporal designs is therefore suggested to rectify the identified issue in HUIM. Preprocessing of time series-based information into fuzzy item sets is the first step of the suggested technique. These feed the Graph Based Ant Colony Optimization (GACO) and Fuzzy C Means (FCM) clustering methodologies used in the Improvised Adaptable FCM (IAFCM) method. The suggested IAFCM technique achieves two objectives: optimal item placement in clusters using GACO; and ii) IAFCM clustering and information decrease in FCM cluster. The proposed technique yields high-quality clusters by GACO. Weighted sequential pattern mining, which considers facts of patterns with the highest weight and low frequency in a repository that is updated over a period, is used to locate the sequential patterns in these clusters. The outcomes of this methodology make evident that the IAFCM with GACO improves execution time when compared to other conventional approaches. Additionally, it enhances information representation by enhancing accuracy while using a smaller amount of memory.

Deep Reinforcement Learning Based PHEV Energy Management With Co-Recognition for Traffic Condition and Driving Style

As one of the key technologies of plug-in hybrid electric vehicle (PHEV), the energy management strategy (EMS) is interactively influenced by driving style, traffic condition as well as vehicle operation state. In this paper, a novel twin delayed deep deterministic police gradient (TD3) algorithm based EMS integrating co-recognition for driving style and traffic condition is proposed, aiming to improve the generalization ability of EMS in various application scenarios with superior energy saving performance and higher self-learning efficiency. In particular, the TD3 based energy management architecture combining delayed policy update and smooth regularization technologies is studied to achieve simultaneous improvement for PHEV energy efficiency and strategy convergence speed. Furthermore, the traffic conditions are recognized by fuzzy C-means method, while the local minimum problem is effectively avoided by incorporating simulated annealing (SA) and genetic algorithm (GA). Sequentially, the driving styles are decoupled from recognized traffic condition, which are further recognized as three typical styles. The comparison results of the proposed strategy with several representative deep reinforcement learning based EMSs indicate that the TD3 based EMS outperforms DDQN and DDPG based EMSs in terms of convergence speed and energy saving performance. With considering the recognized traffic condition and driving style, the energy efficiency of TD3 based EMS is further improved with an ideal robustness to various driving cycles.

A Multiagent Mission Coordination System for Continuous Situational Awareness of Bushfires

This paper devises a multi-agent Mission Coordinating Architecture (MCA) to achieve continuous situational awareness (SA) in bushfires, which can help with quick detection and accurate response to the hazards. In this paper, we use Unmanned Aerial Vehicles (UAVs) as instantiations of physical agents. MCA is a scalable architecture and aims to provide the UAVs with parallel mission plans, adopting a fire spread probability map and the Fuzzy C-means method to avoid mission overlap and duplicate information. The architecture is then enhanced by integrating a synchronized communication framework to facilitate UAVs’ adaptive cooperation and total flight time optimization. Furthermore, integrating the communication framework minimizes the number of deployed UAVs to fully cover the same area, saving considerable cost and energy compared to the Parallel Mode. The scalability challenge of the Parallel Mode, determining the required number of UAVs to cover the entire area, and the efficiency of the mission planning algorithm are thoroughly investigated and compared to the performance of the Communication Mode. Finally, the simulation results prove the MCA’s effectiveness in enhancing the UAVs’ exploration capability, resulting in comprehensive monitoring of all affected areas. Note to Practitioners—The proposed architecture in this research could offer flexibility to increase the number of UAVs on demand without requiring a change and adjustment of the parameters. Furthermore, a synchronized communication framework enhances MCA, enabling all UAVs to share their resources and exploit residual battery time to assist each other, manage the overall operation time, and reduce the total operation cost.

Application of KNN Machine Learning and Fuzzy C-Means to Diagnose Diabetes

The disease is a common thing in humans. Diseases that attack humans do not know anyone and do not know age. The disease experienced by a person starts from an ordinary level until it can be declared severe to the point of being at risk of death. In this study, the early diagnosis was carried out related to diabetes, where diabetes is a condition in which the sufferer’s body has low sugar levels above normal. Symptoms experienced by sufferers include frequent thirst, frequent urination, frequent hunger, and weight loss. Based on these problems, a system is needed that can quickly find out the diagnosis experienced by a patient. This research aimed to diagnose diabetes early on based on early symptoms. The methods used are KNN and web-based fuzzy C-means. Creating a web-based system can represent medical personnel experts in a fast-diagnosing approach to diabetes. This system was a computer program embedded with the knowledge of the characteristics of diabetes. The results of testing the KNN and Fuzzy C-means applications and methods get an accuracy of 96% for the KNearest Neighbor method, while for the Fuzzy C-Means method with Confusion Matrix calculations, an accuracy of 96% is obtained, so it can be concluded that the Fuzzy C-means method Means better than the K-Nearest Neighbor method.

Cluster Barang Elektronik Mengguanakan Algoritma Fuzzy C-Means dengan Optimize Parameter Grid

Electronic products are goods that are really needed at this time, because electronic goods really help humans in carrying out various daily activities, such as television, computers, cellphones, etc. The problem is how to apply the Fuzzy C-means method with Optimize Parameter Grid in the form of grouping electronic goods data for the needs of consumers used, and how to determine the optimum number of clusters from the use of the method used in grouping electronic goods data sets to find the best accuracy value. The purpose of this study was to apply the use of the fuzzy c-means algorithm in the case of grouping electronic data and produce an output to find out the best value of the electronic data used. While this research uses one method, namely the fuzzy cmeans algorithm with Optimize grid parameters which are included in the grouping rules in data mining. The research results are expected to be able to find the best electronic data set grouping based on the Davies Bouldin Index value resulting from the analysis of the fuzzy c-means algorithm at measure_type : NumericalMeasure, Dbi = 0.516 and measure_type : MixedMeasure, Dbi = 0.627.

A Market Segmentation Study of Solo Travel Intentions and Constraints

This study provides a market segmentation analysis of the prospective solo travel market based on perceived travel constraints. The data were collected from 1,017 Australian consumers. Using the Fuzzy C-Mean method, the study identified three segments: highly constrained, soloist, and socializer. The segments were distinctive in terms of perceived constraints and solo travel attitudes and intentions. The differences among the segments were investigated further using the concept of self-construal, measured by the level of autonomy and relatedness. Significant differences in sociodemographic characteristics were observed, especially regarding gender, age, and solo dwelling status. Practical recommendations are provided to inform strategies intended to offset travel constraints and create tourism experiences that cater to the needs of different prospective solo travel segments.

A novel fuzzy approach to identify the phenotypic adaptability of common bean lines

The genotype by environment interaction is the main factor that influences the response of evaluated genotypes in trials of value for cultivation and use. Adaptability and stability analyses are fundamental to understanding the performance of genotypes in a growing region. Some of these methodologies incorporate previous information for recommending an extra group of genotypes denominated as specific ideotypes under certain cultivation conditions. Based on this strategy, the centroid method and its modifications have been widely used due to the simplicity of classification of the evaluated genotypes. However, these methodologies present problems in identifying adaptability patterns of some genotypes. Artificial intelligence techniques, such as fuzzy C-means, can be an alternative to reduce these difficulties, since they use, in addition to distance information between genotypes, memberships (measures quantifying how much an observation belongs to a particular class) to increase discriminatory power. Therefore, our aim was to propose and evaluate the phenotypic adaptability method by fuzzy clustering to assist cultivar recommendations. The adaptation of the fuzzy C-Means method to classify the genotypes was implemented in BioFuzzy software. The grain yield data of black common bean genotypes were used to evaluate the potential of the method. The results obtained by this method were compared with those obtained by the centroid method. The phenotypic adaptability method by fuzzy clustering was effective in identifying the adaptability patterns of common bean genotypes. Moreover, the discriminatory power was higher than that observed with the centroid method.

A semiparametric clustering method for the screening of retired Li-ion batteries from electric vehicles

This study proposes a semiparametric clustering method (SPCM) to screen the batteries retired from electric vehicles (EVs) for echelon utilization. To quickly capture the static and dynamic battery characteristics, a hybrid test combining the low-current hybrid pulse power characterization (L-HPPC) test and China light-duty vehicle test cycle (CLTC) is designed. The pseudo-two-dimensional (P2D) model is combined with genetic algorithm (GA), and model parameters are identified by the hybrid test. Then, a clustering matrix containing sensitive model parameters is established and processed by principal component analysis (PCA). In SPCM, the Euclidean-distance-based clustering matrix is divided into high-density and low-density parts through a predetermined mixing proportion. For high-density data, hierarchical clustering (HC) is used to determine the initial cluster centers, and fuzzy C-means (FCM) method is utilized to determine the core clusters, while the low-density data is assigned to the nearest neighbours. Finally, 18 open-access datasets are employed to validate the effectiveness of the proposed SPCM. These results indicate that the proposed SPCM is superior to K-mean, K-medoid, and FCM in terms of normalized mutual information (NMI). Furthermore, the pulse testing and capacity testing of the re-grouped retired batteries further demonstrate the effectiveness of the SPCM in the screening of retired batteries.

Business Policy and Competitiveness of Farmers’ Organizations—Empirical Evidence from Taiwan

Sustainable competitiveness is at the core of effective organizational management. However, the interpretation of the concept of competitiveness is quite divided, especially for organizations that are positioned as social enterprises and must bear profits and losses. In this context, we used the PLS-SEM method to discuss the impact of business policies on organizational resource management and select the Taiwanese farmers’ associations for analysis. According to the resource-dependent model, we selected the corresponding variables from the existing operational competition indicators to represent the four major business sectors of these associations. The main contribution of this study is that through expert opinion, the evaluation indicators of the existing business performance competition can be integrated to define the competitiveness of farmers’ associations. We also used the fuzzy C-means method cluster analysis to effectively divide 279 farmers’ associations into six groups according to “population, land, and industry”. It is possible to evaluate the competitiveness of the departments and business policies of the farmers’ associations in different groups according to the performance of their departments. Presenting the differences in business policies will help the government implement effective counseling services between regions. Competitiveness based on the performance of the main functions significantly affects the business execution, asset allocation, marketing, and sales activities of the farmers’ associations. This article clearly points out that an organization may achieve a competitive business objective by taking stock of its own operating and financial conditions. This is also true for non-profit social enterprises.

Assessment decisions of independent learning activities using SMART–FCM method

Sekolah Tinggi Ilmu Komputer (STIKOM) PGRI Banyuwangi implemented the Independent Learning - Independent Campus (MBKM) activity for two semesters. The results of student assessments for MBKM activities for one semester are influenced by the results of daily and weekly logbook monitoring, monitoring and evaluation assessments, and assessments of supervisors, examiners, and work partners. Assessments that are less objective cause many students to get good grades even though the implementation of MBKM activities is not well. The Simple Multi-Attribute Rating Technique (SMART) method is used to produce student eligibility group data and a more objective assessment. The results of the SMART calculations are combined with the Fuzzy C-Means (FCM) algorithm so that the results of grouping student data are more appropriate based on the similarities and characteristics of the members. Silhouette Coefficient is used to compare the grouping results. The results obtained that the use of SMART-FCM is better than the SMART results because it has a Silhouette Coefficient value close to 1 of 0.31187.

Comparison K-Means and Fuzzy C-Means Methods to Grouping Human Development Index Indicators in Indonesia

The Human Development Index (HDI) is an important indicator to measure the success of efforts to improve people's quality of life. The increase in the human development index in Indonesia is not accompanied by an even distribution of the human development index in every district/city in Indonesia. To facilitate the government in making policies and plans in overcoming the uneven HDI in Indonesia, it is necessary to group districts/cities in Indonesia based on HDI indicators. This study discusses the use of the K-means and Fuzzy C-Means algorithms with a total of 4 clusters. The grouping results obtained summarize that most districts/cities in Papua Island have low HDI indicators. The achievement of the HDI indicator in the medium category on the K-Means and Fuzzy C-Means methods is the same, spread across all major islands in Indonesia. However, the Nusa Tenggara Islands generally have a medium HDI indicator achievement. The achievements of the HDI indicators with high categories in the K-Means and Fuzzy C-Means methods are mostly found on the islands of Sumatra, Java, Kalimantan, and Sulawesi. The achievement of the HDI indicator in the very high category in the K-Means and Fuzzy C-Means methods is found in provincial capitals in several provinces in Indonesia as well as in big cities in Indonesia. The results of this study indicate that the S_DBW index and C_index values of the Fuzzy c-means method are smaller than the K-Means method, namely 2.312 and 0.105.

Serum Fatty Acid Composition Balance by Fuzzy C-Means Method in Individuals with or without Metabolic Dysfunction-Associated Fatty Liver Disease.

Circulating fatty acid composition is assumed to play an important role in metabolic dysfunction-associated fatty liver disease (MAFLD) pathogenesis. This study aimed to investigate the association between the overall balance of serum fatty acid composition and MAFLD prevalence. This cross-sectional study involved 400 Japanese individuals recruited from a health-screening program. We measured fatty acids in serum lipids using gas chromatography-mass spectrometry. The serum fatty acid composition balance was evaluated using fuzzy c-means clustering, which assigns individual data points to multiple clusters and calculates the percentage of data points belonging to multiple clusters, and serum fatty acid mass%. The participants were classified into four characteristic subclasses (i.e., Clusters 1, 2, 3, and 4), and the specific serum fatty acid composition balance (i.e., Cluster 4) was associated with a higher MAFLD prevalence. We suggest that the fuzzy c-means method can be used to determine the circulating fatty acid composition balance and highlight the importance of focusing on this balance when examining the relationship between MAFLD and serum fatty acids.

Indonesian Territory Clustering Based On Harvested Area and Rice Productivity Using Clustering Algorithm

Rice (Latin: Oryza sativa) is one of the most important cultivated plants in civilization. This plant is the main commodity for almost all Indonesian people. Indonesia is in third place as the largest rice producing country in the world. However, based on data from the Statistics Indonesia, Indonesia will still import rice until 2022. The transfer of paddy fields is one of the reasons why Indonesia is still importing rice to this day. Many lands that used to be paddy fields have turned into airports, industrial land, housing, and so on. Rice production is one of the important topics to be discussed in order to develop rice production in areas that are still relatively low. The purpose of this research is to classify cities/regencies in Indonesia based on rice production data in 2021. In this study, three clustering methods were used, namely, Partitioning Around Medoid (PAM), Clustering Large Applications (CLARA) and Fuzzy C-Means (FCM). Then the three methods are compared based on their silhouette coefficient values. The best obtained method is FCM method with two clusters and a silhouette value of 0.828. Results clustering with the best method is used as a reference in making maps clustering. Areas that are still relatively low are expected to increase rice productivity.

Optimal placement and capacity sizing of energy storage systems via NSGA-II in active distribution network

In recent years, with the rapid development of renewable energy, the penetration rate of renewable energy generation in the active distribution network (ADN) has increased. Because of the instability of renewable energy generation, the operation stability of ADN has decreased. Due to the ability to cut peak load and fill valley load, battery energy storage systems (BESSs) can enhance the stability of the electric system. However, the placement and capacity of BESSs connected to ADN are extremely significant, otherwise, it will lead to a further decline in the stability of ADN. To ensure the effectiveness of the BESSs connected to the grid, this work uses the fuzzy kernel C-means (FKCM) method for scene clustering. Meanwhile, a multi-objective optimization model of BESS configuration is established with the objective of BESS configuration cost, voltage fluctuation, and load fluctuation, and solved by non-dominated sorting genetic algorithm-II (NSGA-II). In this work, the grey target decision method based on the entropy weight method (EWM) is used to obtain the optimal compromise solution from the Pareto non-dominated set. Moreover, the proposed method is tested and verified in the extended IEEE-33 node system and the extended IEEE-69 node system. The results show that the BESSs configuration scheme obtained by NSGA-II can effectively reduce the fluctuation of voltage and load, and improve the stability of ADN operation.

Pro-Ecological Energy Attitudes towards Renewable Energy Investments before the Pandemic and European Energy Crisis: A Segmentation-Based Approach

The household as the primary decision-making unit is founded on classical and neoclassical economics. However, household behaviour changes have been noticeable in the last decade, moving towards more green and sustainable patterns, which have been pronounced in EU countries striving for a more significant share of renewable energy sources (RES) in energy consumption. These behaviours can be attributed to sustainable economics and are an essential part of energy transformation, as they are focused on pro-ecological attitudes, considering both financial activities and those related to caring for the environment and future generations. This article aims to segment energy consumers and to determine what attitudes prevailed in the selected segments and to what extent consumers were pro-ecologically oriented when making decisions regarding RES management before the pandemic and the energy crisis outbreak in Europe. We propose a three-segment model for archetyping household energy consumers in Poland by considering the following groups of factors: environmental and energy goods protection (F1), the mirror effect (F2), and energy and devices profitability (F3). The segments are distinguished based on factor analysis and the fuzzy c-means method. The number of segments is determined based on the cluster validity measure. The presented results prove that the F1 factor plays the leading role in each segment. The percentage of positive responses for each segment, including a migrating group of households, oscillates over 80%. It gives strong hope for retaining sustainable attitudes regardless of the pandemic and energy crisis that occurred in 2022 in the EU.

Efficient Extraction of Coastal Upwelling in the Northern South China Sea Based on Sea Surface Temperature Data and Research of Its Interannual Variations

By applying of the spatial intuitionistic fuzzy c-means (SIFCM) method based on MODIS Terra Sea Surface Temperature (SST) data, we extract the significant coastal upwelling in the northern South China Sea (NSCS) efficiently, without the need for setting thresholds or regional connectivity processing. In this letter, the procedure is applied to the 8-day SST data from 2001 to 2021. Accordingly, the extraction data are effectively utilized to obtain long-term characteristics, based on which the interannual variations are quantitatively investigated and examined. The long-term characteristics data are analysed in combination with the Oceanic Niño Index (ONI), and the results indicate that the yearly average SST differences of summer coastal upwelling are positively correlated with the El Niño Southern Oscillation (ENSO).

Ant Colony Optimization-Enabled CNN Deep Learning Technique for Accurate Detection of Cervical Cancer.

Cancer is characterized by abnormal cell growth and proliferation, which are both diagnostic indicators of the disease. When cancerous cells enter one organ, there is a risk that they may spread to adjacent tissues and eventually to other organs. Cancer of the cervix of the uterus often initially manifests itself in the uterine cervix, which is located at the very bottom of the uterus. Both the growth and death of cervical cells are characteristic features of this condition. False-negative results provide a significant moral dilemma since they may cause women to get an incorrect diagnosis of cancer, which in turn can result in the woman's premature death from the disease. False-positive results do not raise any significant ethical concerns; but they do require a patient to go through an expensive and time-consuming treatment process, and they also cause the patient to experience tension and anxiety that is not warranted. In order to detect cervical cancer in its earliest stages in women, a screening procedure known as a Pap test is often performed. This article describes a technique for improving images using Brightness Preserving Dynamic Fuzzy Histogram Equalization. To individual components and find the right area of interest, the fuzzy c-means approach is applied. The images are segmented using the fuzzy c-means method to find the right area of interest. The feature selection algorithm is the ACO algorithm. Following that, categorization is carried out utilizing the CNN, MLP, and ANN algorithms.