Fuzzy C-means Method Research Articles

Brain Tumour Region Extraction Using Novel Self-Organising Map-Based KFCM Algorithm

Medical professionals need help finding tumours in the ground truth image of the brain because the tumours’ location, contrast, intensity, size, and shape vary between images because of different acquisition methods, modalities, and the patient’s age. The medical examiner has difficulty manually separating a tumour from other parts of a Magnetic Resonance Imaging (MRI) image. Many semi- and fully automated brain tumour detection systems have been written about in the literature, and they keep improving. The segmentation literature has seen several transformations throughout the years. An in-depth examination of these methods will be the focus of this investigation. We look at the most recent soft computing technologies used in MRI brain analysis through several review papers. This study looks at Self-Organising maps (SOM) with K-means and the kernel Fuzzy c-means (KFCM) method for segmenting them. The suggested SOM networks were first compared to K-means analysis in an experiment based on datasets with well-known cluster solutions. Later, the SOM is combined with KFCM, reducing time complexity and producing more accurate results than other methods. Experiments show that skewed data improves networks’ performance with more SOMs. Finally, performance measures in real-time datasets are analysed using machine learning approaches. The results show that the proposed algorithm has good sensitivity and better accuracy than k-means and other state-of-art methods.

Load combination feedback of fracture in concrete dams based on monitoring data with simplified fuzzy association rules

The existed cracks are serious potential threats to the integrity and safety of concrete dams, and may even lead to dam failure and flood disaster. The fracture behavior in concrete dams is influenced by a kaleidoscope of load effects, which act in combination with a complicated relationship. A quantitative analysis method with improved association rule mining (ARM) based on monitored crack opening is proposed in this study. Fuzzy Set theory and Fuzzy C-Means method are introduced to establish the fuzzy ARM method for sequential monitoring data, and the traditional Apriori algorithm is simplified based on the single decision attribute. The feasibility and efficiency of the proposed method is validated by its application in Chencun gravity arch dam with a large structural fracture, and low temperature and low upstream water level are identified as the detrimental load combination for propagation of the fracture in this dam.

PENERAPAN FUZZY C MEANS PADA NILAI NDVI LANDSAT 8 UNTUK KLASTERISASI KEHIJAUAN KELURAHAN DI KOTA BOGOR

This study aims to cluster sub-districts (kelurahan) in Bogor Municipality based on greenness level. Normalized Difference Vegetation Index (NDVI) values were processed from Landsat 8 OLI recorded on 24 May 2020, downloaded from United States Geological Survey (USGS) website. NDVI values greater than 0,3 indicate that vegetation pixels are separated from overall raster maps. These NDVI values over 0,3 were extracted based on each sub-district poligon within Bogor Municipality. For sub-districts with NDVI 0,3, the percentage of the area and NDVI mean values were generated using Geographic Information System (GIS). In order to cluster 68 sub-districts in Bogor Municipality, two variables of NDVI, namely area percentage and mean NDVI values, were processed using the Fuzzy C Means (FCM) method. Greenness level clustering using the FCM method shows 14 sub-districts in high class, 28 in medium class, and 26 in low cluster class. Overlay analysis among clusters shows two sub-districts (7.69%) in the low cluster class inside the medium class, one sub-district (3.57%) in the medium class within the low class, and one sub-district (7.14%) in the high cluster class inside the medium class. There are two main indications for an overlapping sub-district located in multiple clusters, namely the sub-district that has little different values with neighbouring cluster centres, and the sub-district that has similar different values with two cluster centres.

Analysis of Self-Organizing Maps and Fuzzy C-Means methods in Clustering Teacher Data for Nominations of Candidates for Education Unit Supervisors

The quality of education is very dependent on the management of education management one of the important factors in the management of education is monitoring and evaluation. Therefore, continuous Education Supervision supported by the appointment of competent supervisors will have implications for the quality of education. Supervision of education management is carried out by the principal or teacher appointed to carry out managerial and academic supervision in certain educational units. The problem that often occurs is that grouping teacher data to be selected as supervisor candidates are still conventional. Therefore, a teacher data grouping model is needed to obtain useful information in planning strategic steps and policy regulations for determining prospective supervisors for academic units. By utilizing Information and Communication Technology, especially in the field of Data Mining, this Teacher Data Grouping research uses the Fuzzy C-Means and Self Organizing Maps method, and the grouping results are analyzed by measuring the spread of data on each grouping formed by using cluster variance. The output of the Teacher Data grouping process using the Fuzzy C-Means and Self Organizing Maps methods can bring up a group of Teacher Data nominations that are competent to be selected as candidates for supervisors for certain educational units. The research results were obtained by forming several groupings in Fuzzy C-Means by providing an error accuracy value of 0.1 and Self Organizing Maps, which is set by the learning rate and learning rate; the results obtained are grouping with 3 clusters by providing a learning rate of 0.8 and a learning rate of 0.7 in the Self Organizing Maps method has a Variant value that is ideal compared to grouping on Fuzzy C-Means and rather than the same method by forming different groups.

Strategi Pengembangan Klaster UMKM Menggunakan Metode Analytic Hierarchy Process Di Kabupaten Kutai Timur

Micro, small and medium enterprises (MSMEs) had an important role in the Indonesian economy, with the creation of domestic products and the ability to absorb labor. In order to maintain the performance of MSMEs to exist and grow up, it was necessary to have a strategy carried out both from internal MSMEs themselves or the stakeholders. The aims of this study was to find out the strategy for developing the MSMEs cluster by using the analytic hierarchy process method. The determination of the sample used was by purposive sampling, namely members of the MSMEs home industry partner of LPB Pabanet, who were active and willing to be respondents with a total of 27 MSMEs. This study used a direct interview to respondents containing questions for clustering MSMEs, then a focus group discussion (FGD) was carried out with experts which aims to explore information used in formulating development strategies of MSME based on clusters. The results showed that based on the analysis using the fuzzy C-means method formed 3 clusters of SMEs. The strategy for the first cluster was on the production process criteria (0,391) with an alternative strategy for obtaining permits of P-IRT (0,351). In second cluster, the criteria with the highest weight was capital (0,395) with alternative strategies for making financial reports regularly (0,863). In the third cluster, the alternative strategy chosen on the marketing criteria (0,547) was to establish a partnership (0,407).

Atlas-Based Segmentation in Extraction of Knee Joint Bone Structures from CT and MR.

The main goal of the approach proposed in this study, which is dedicated to the extraction of bone structures of the knee joint (femoral head, tibia, and patella), was to show a fully automated method of extracting these structures based on atlas segmentation. In order to realize the above-mentioned goal, an algorithm employed automated image-matching as the first step, followed by the normalization of clinical images and the determination of the 11-element dataset to which all scans in the series were allocated. This allowed for a delineation of the average feature vector for the teaching group in the next step, which automated and streamlined known fuzzy segmentation methods (fuzzy c-means (FCM), fuzzy connectedness (FC)). These averaged features were then transmitted to the FCM and FC methods, which were implemented for the testing group and correspondingly for each scan. In this approach, two features are important: the centroids (which become starting points for the fuzzy methods) and the surface area of the extracted bone structure (protects against over-segmentation). This proposed approach was implemented in MATLAB and tested in 61 clinical CT studies of the lower limb on the transverse plane and in 107 T1-weighted MRI studies of the knee joint on the sagittal plane. The atlas-based segmentation combined with the fuzzy methods achieved a Dice index of 85.52-89.48% for the bone structures of the knee joint.

Extraction and Quantification of Words Representing Degrees of Diseases: Combining the Fuzzy C-Means Method and Gaussian Membership

Due to the development of medical data, a large amount of clinical data has been generated. These unstructured data contain substantial information. Extracting useful knowledge from this data and making scientific decisions for diagnosing and treating diseases have become increasingly necessary. Unstructured data, such as in the Marketplace for Medical Information in Intensive Care III (MIMIC-III) data set, contain several ambiguous words that demonstrate the subjectivity of doctors, such as descriptions of patient symptoms. These data could be used to further improve the accuracy of medical diagnostic system assessments. To the best of our knowledge, there is currently no method for extracting subjective words that express the extent of these symptoms (hereinafter, "degree words"). Therefore, we propose using the fuzzy c-means (FCM) method and Gaussian membership to quantify the degree words in the clinical medical data set MIMIC-III. First, we preprocessed the 381,091 radiology reports collected in MIMIC-III, and then we used the FCM method to extract degree words from unstructured text. Thereafter, we used the Gaussian membership method to quantify the extracted degree words, which transform the fuzzy words extracted from the medical text into computer-recognizable numbers. The results showed that the digitization of ambiguous words in medical texts is feasible. The words representing each degree of each disease had a range of corresponding values. Examples of membership medians were 2.971 (atelectasis), 3.121 (pneumonia), 2.899 (pneumothorax), 3.051 (pulmonary edema), and 2.435 (pulmonary embolus). Additionally, all extracted words contained the same subjective words (low, high, etc), which allows for an objective evaluation method. Furthermore, we will verify the specific impact of the quantification results of ambiguous words such as symptom words and degree words on the use of medical texts in subsequent studies. These same ambiguous words may be used as a new set of feature values to represent the disorders. This study proposes an innovative method for handling subjective words. We used the FCM method to extract the subjective degree words in the English-interpreted report of the MIMIC-III and then used the Gaussian functions to quantify the subjective degree words. In this method, words containing subjectivity in unstructured texts can be automatically processed and transformed into numerical ranges by digital processing. It was concluded that the digitization of ambiguous words in medical texts is feasible.

Deep Reinforcement Learning for the Detection of Abnormal Data in Smart Meters

The rapidly growing power data in smart grids have created difficulties in security management. The processing of large-scale power data with the use of artificial intelligence methods has become a hotspot research topic. Considering the early warning detection problem of smart meters, this paper proposes an abnormal data detection network based on Deep Reinforcement Learning, which includes a main network and a target network composed of deep learning networks. This work uses the greedy policy algorithm to find the action of the maximum value of Q based on the Q-learning method to obtain the optimal calculation policy. It also uses the reward value and discount factor to optimize the target value. In particular, this study uses the fuzzy c-means method to predict the future state information value, which improves the computational accuracy of the Deep Reinforcement Learning model. The experimental results show that compared with the traditional smart meter data anomaly detection method, the proposed model improves the accuracy of meter data anomaly detection.

Comparison of Traditional Image Segmentation Methods Applied to Thermograms of Power Substation Equipment

The variation in the thermal state of electrical energy substation equipment is normally associated with natural wear or equipment failure. This can be detected by infrared thermography, but technically it demands a long time to analyze these images. Computational analysis can allow an automated, more agile, and more efficient analysis to detect overheated regions in thermographic images. Therefore, it is necessary to segment the region of interest in the images; however, the results may diverge depending on the technique used. Thus, this article presents the improvement of four different techniques implemented in Python and applied in a substation under real operating conditions for a period of eleven months. The performance of the four methods was compared using eight statistical performance measures, and the efficiency was measured by the runtime. The segmentation results showed that the methods based on a threshold (Otsu and Histogram-Based Threshold) were fast, with processing times of 0.11 to 0.24 s, but caused excessive segmentation, presenting the lowest accuracy (0.160 and 0.444) and precision (0.004 and 0.049, respectively). The clustering-based methods (Cluster K-means and Fuzzy C-means) showed similar results to each other but were more accurate (0.936 to 1.000), more precise (0.965 to 1.000), and slower, with 2.55 and 38.8 s, respectively, compared to the threshold methods. The Fuzzy C-means method obtained the highest values of specificity, accuracy, and precision among the methods under analysis, followed by the Cluster K-means method.

RETRACTED ARTICLE: Comparative analysis of improved FCM algorithms for the segmentation of retinal blood vessels

The main purpose of identifying and locating the retina vessels is to specify from the fundus image the various tissues of the vascular structure, which can be wide or tight. The classification of vessels in the retinal image often confronts several challenges, such as the low contrast accompanying the fundus image, the inhomogeneity of the background lighting, and the noise. Moreover, fuzzy c-means (FCM) is one of the most frequently used algorithms for medical image segmentation due to its effectiveness. Hence, many FCM method derivatives have been developed to improve their noise robustness and time-consuming. This paper aims to analyze the performance of some improved FCM algorithms to recommend the best ones for the segmentation of retinal blood vessels. Eight derivatives of FCM algorithm are detained in this study: FCM, EnFCM, SFCM, FGFCM, FRFCM, DSFCM_N, FCM_SICM and SSFCA. The performance analysis is conducted from three viewpoints: noise robustness, blood vessels segmentation performance, and time-consuming. At first, the noise robustness of improved FCM clustering algorithms is evaluated using a synthetic image degraded by various types and levels of noise. Then, the ability of the selected algorithms to segment retinal blood vessels is assessed based on images from the DRIVE and STARE databases after a pre-processing phase. Finally, the time consumption of each algorithm is measured. The experiments demonstrate that the FRFCM and DSFCM_N algorithms achieve better results in terms of noise robustness and blood vessels segmentation. Regarding the running time, the FRFCM algorithm requires less time than other algorithms in the segmentation of retinal images. The results of this study are extensively discussed, and some suggestions are proposed at the end of this paper.

PENGELOMPOKAN PENYANDANG MASALAH KESEJAHTERAAN SOSIAL DI JAWA BARAT MENGGUNAKAN K-MEANS DAN FUZZY C-MEANS

Social welfare problems still occur in some provinces in Indonesia, including in West Java. Social welfare problems cannot be completely overcome, but according to policy perceptions, they can be reduced, therefore analyses are required. Grouping data on people with social welfare problems to find out the best group based on the data will provide alternative policies and appropriate methods. The purpose of this study was to find the best group of people with social welfare problems using the k-means and fuzzy c-means methods based on the results of the DBI evaluation. The methods used for this grouping were the k-means and fuzzy c-means algorithm methods. From the results of this study, it was obtained the best 2 groups from the experiment of fuzzy c-means algorithms based on the smallest DBI assessment or close to 0 between the k-means and fuzzy c-means algorithms from each DBI value, they were k-means algorithm with value of 0.029 and fuzzy c-means algorithm with value of 0.006.

Abdominal vessel segmentation using vessel model embedded fuzzy C-means and similarity from CT angiography.

The accurate abdominal vessel segmentation of CT angiography (CTA) data is essential for diagnosis and surgical planning. However, accurate abdominal vessel segmentation is a difficult problem since the following challenges: (1) complex abdominal vessel structure containing a wide range size of vessel branches, (2) low contrast of small vessels, and (3) uneven distribution of vessel grayscale. With full consideration of the challenges, we propose an automatic vessel segmentation algorithm. For challenge 1, the algorithm's framework is divided into large and small vessel segmentation and has the following steps. Firstly, a vessel model embedded fuzzy c-means (VMEFCM) method with full consideration of challenge 2 is presented to obtain the initial vessel voxels. Then, considering challenge 3, a large vessel segmentation method based on the initial vessel voxels, similarity, and morphologic is proposed. Finally, a small vessel segmentation method based on spine is described. Extensive analysis is carried out on simulation datasets and 78 CTA datasets. The experimental results indicate that each step of the algorithm achieves the prospective results, and the proposed algorithm is effective and accurate with low computational cost. The dice, sensitivity, Jaccard coefficient, and precision rate were 93.7±2.8%, 93.7±2.8%, 88.2±4.8%, and 94.2±7.5% respectively.

Comparison of Hierarchical Clustering, K-Means, K-Medoids, and Fuzzy C-Means Methods in Grouping Provinces in Indonesia according to the Special Index for Handling Stunting

Stunting has been widely known as the highest case of malnutrition suffered by toddlers in the world and has a bad impact on children's future. In 2018, Indonesia was ranked the 31st highest stunting in the world and ranked 4th in Southeast Asia. About 30.8% (roughly 3 out of 10) of children under 5 years suffer from stunting in Indonesia. To support the government policy making in handling stunting, it is undoubtedly necessary to classify the levels of stunting handling in regions in Indonesia. In this work, the hierarchical agglomerative and non-hierarchical clustering is compared and evaluated to perform clustering on stunting data. The agglomerative hierarchical cluster uses Single Linkage, Average Linkage, Complete Linkage, and Ward Method, while the non-hierarchical cluster uses K-Means, K-Medoids (PAM) Clustering, and Fuzzy C-Means. This study uses data from 12 IKPS indicators in 34 provinces in Indonesia in 2018. Based on the results of the evaluation using the Connectivity Coefficient, Dunn Index, Silhouette Coefficient, Davies Bouldin Index, Xie & Beni Index, and Calinski-Harabasz Index, the results show that the Average Linkage is the best cluster method with the optimal number of clusters is four clusters. The first cluster is a cluster with a good level of stunting management which consists of 28 provinces. The second cluster consists of only one province, DI Yogyakarta with a very good level of stunting handling. The third cluster consists of four provinces with poor stunting handling rates. Finally, the last cluster consisting of one province, Papua, has a very poor level of stunting handling.

Spatial Fuzzy C-Means Clustering Analysis of U.S. Presidential Election and COVID-19 Related Factors in the Rustbelt States in 2020

The rustbelt states play a key role in determining the vote turnout in the U.S. elections. The current study attempts to utilize the spatial fuzzy C-means method to analyze the U.S. presidential election in the rustbelt states in 2020. We intend to explore that the U.S. presidential election had related factors, including COVID-19-related factors, such as the mask-wearing percentage and the COVID-19 death tolls in each county of the rust belt states. Contrary to the related literature, the study uses education level, number of house units, unemployment rate, household income, COVID-19-related factors and the share of Republican’s votes in the presidential election. The results indicate that spatial generalized fuzzy C-means analysis has better clustering results than the C-means clustering method. Moreover, the COVID-19 death toll in each county did not affect the Republican’s vote share in the rustbelt states, while the mask-wearing behavior in some regions had a negative impact on the Republican’s vote share.

Outlier Detection Algorithm Based on Fuzzy CMeans and Self-organizing Maps Clustering Methods

Outlier Detection Algorithm Based on Fuzzy CMeans and Self-organizing Maps Clustering Methods

Evaluation of Dose-Painting in the Dominant Intraprostatic Lesions by Radiobiological Parameters using 68Ga- PSMA PET/CT.

Background: Patients diagnosed with dominant intraprostatic lesions (DIL) may need radiation doses over than 80 Gy. Dose-painting by contours (DPC) is a useful technique which helps the patients. Dose-painting approach need to be evaluated.Objective: To evaluate the DCP technique in the case of boosting the DILs by radiobiological parameters, tumor control probability (TCP), and normal tissue complication probability (NTCP) via PET/CT images traced by 68Ga-PSMA.Material and Methods: In this analytical study, 68Ga-PSMA PET/CT images were obtained from patients with DILs that were delineated using the Fuzzy c-mean (FCM) algorithm and thresholding methods. The protocol of therapy included two phases; at the first phase (ph1), a total dose of 72 Gy in 36 fractions were delivered to the planning target volume (PTV1); the seconds phase consisted of the application of variable doses to the PTV2. Moreover, two concepts were also considered to calculate the TCP using the Zaider-Minerbo model.Results: The lowest volume in DILs belonged to the DIL1 extracted by the FCM method. According to dose-volume parameters of the rectum and bladder, by the increase in the PTV dose higher than 92 Gy, the amounts of rectum and bladder doses are increased. There was no difference between the TCPs of DILs at doses higher than 86 Gy and 100 Gy for ordinary and high clone density, respectively.Conclusion: Consequently, our dose-painting approach for DILs, extracted by the FCM method via PET/CT images, can reduce the total dose for prostate radiation with 100% tumor control and less normal tissue complications.

Distributed Semisupervised Fuzzy Regression With Interpolation Consistency Regularization

Recently, distributed semi-supervised learning (DSSL) algorithms have shown their effectiveness in leveraging unlabeled samples over interconnected networks, where agents cannot share their original data with each other and can only communicate non-sensitive information with their neighbors. However, existing DSSL algorithms cannot cope with data uncertainties and may suffer from high computation and communication overhead problems. To handle these issues, we propose a distributed semi-supervised fuzzy regression (DSFR) model with fuzzy if-then rules and interpolation consistency regularization (ICR). The ICR, which was proposed recently for semi-supervised problem, can force decision boundaries to pass through sparse data areas, thus increasing model robustness. However, its application in distributed scenarios has not been considered yet. In this work, we proposed a distributed Fuzzy C-means (DFCM) method and a distributed interpolation consistency regularization (DICR) built on the well-known alternating direction method of multipliers to respectively locate parameters in antecedent and consequent components of DSFR. Notably, the DSFR model converges very fast since it does not involve back-propagation procedure and is scalable to large-scale datasets benefiting from the utilization of DFCM and DICR. Experiments results on both artificial and real-world datasets show that the proposed DSFR model can achieve much better performance than the state-of-the-art DSSL algorithm in terms of both loss value and computational cost.

Fracture Structures in and Around Hakone Volcano Revealed by Dense Seismic Observations

Preexisting fracture systems, including old fissures, dikes, and microfractures in the caldera, are possibly used as channels for magma and hydrothermal fluid intrusions during an eruption. To reveal such a fracture system in the Hakone volcano, we used the fuzzy c-means method to perform clustering on S-wave splitting analysis results. The results show that the fracture system in the Hakone caldera can be divided into two clusters (A and B) or four clusters (A1, A2, B1, and B2). In the central cone vicinity, craters or dikes corresponding to the compressive axis of the regional stress field are dominant, whereas the fault systems with the best orientation to the regional stress field develop around the central cone. Cluster B1 can be explained by the northwest–southeast alignment of micro cracks or dikes corresponding to the direction of maximum horizontal pressure of the regional stress field. The others are likely explained by fault fracture zones, which have an optimal orientation for regional stress fields, or by the alignment of micro cracks affected by the local stress field. Cluster B2 suggests the existence of fracture zones of the Tanna and Hirayama fault systems, which cross the Hakone volcano from north to south. Clusters A1 and A2 are possibly explained by the conjugate system of B2. However, the alignment of micro cracks generated by the local stress field or old volcanic structures can also be a cause of the clusters.

Enhanced radial basis function neural network for tomato plant disease leaf image segmentation

Primary crop losses in agriculture are due to leaf diseases, which farmers cannot identify early. If the diseases are not detected early and correctly, then the farmer will have to undergo huge losses. Therefore, in the field of agriculture, the detection of leaf diseases in tomato crops plays a vital role. Recent advances in computer vision and deep learning techniques have made disease prediction easy in agriculture. Tomato crop front side leaf images are considered for research due to their high exposure to diseases. The image segmentation process assumes a significant role in identifying disease affected areas on tomato leaf images. Therefore, this paper develops an efficient tomato crop leaf disease segmentation model using an enhanced radial basis function neural network (ERBFNN). The proposed ERBFNN is enhanced using the modified sunflower optimization (MSFO) algorithm. Initially, the noise present in the images is removed by a Gaussian filter followed by CLAHE (contrast-limited adaptive histogram equalization) based on contrast enhancement and un-sharp masking. Then, color features are extracted from each leaf image and given to the segmentation stage to segment the disease portion of the input image. The performance of the proposed ERBFNN approach is estimated using different metrics such as accuracy, Jaccard coefficient (JC), Dice's coefficient (DC), precision, recall, F-Measure, sensitivity, specificity, and mean intersection over union (MIoU) and are compared with existing state-of-the-art methods of radial basis function (RBF), fuzzy c-means (FCM), and region growing (RG). The experimental results show that the proposed ERBFNN segmentation model outperformed with an accuracy of 98.92% compared to existing state-of-the-art methods like RBFNN, FCM, and RG, as well as previous research work.

Perhitungan Estimasi Upaya Pengembangan Software Pulsa Online dengan Fuzzy C-Means dan Fuzzy K-Means

Top-up can be done by prepaid and postpaid. Top-up at this time can also be done by way of online purchases. Credit with a prepaid system is a real-time top-up. Payments are made before the customer uses credit. Prepaid credit is different from postpaid which is not real-time and is done after the customer uses credit. Before credit can be used, it is necessary to create a credit server first. In this case, limited resources become an obstacle in completing the credit server creation which will later be used by credit users. Therefore, it is necessary to estimate the effort in the development of the server application, so that the estimation can be known, both in terms of resources, and processing time to estimates in terms of costs. In this case, the appropriate method should be used to overcome the obstacle and reduce the risk of software development. There are several ways to use the Fuzzy K-Mean and Fuzzy C-Means methods to complete the creation of impulse servers, perform analysis and interpretation, and provide information and actions for the quality of research output, education, and evaluation research. The result of the grouping comparison is to produce a derivative formula for the Fuzzy K-Mean and Fuzzy C-Means algorithms.